{
 "cells": [
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   "source": [
    "# An Introduction to High-Dimensional Time Series Forecasting with Neural Networks\n",
    "\n",
    "**Note**: for a high-level overview on this topic, check out my [blog post](https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/). \n",
    "\n",
    "This notebook aims to demonstrate in python/keras code how a sequence-to-sequence neural network can be built for the purpose of time series forecasting. In particular, it explores the \"high-dimensional\" time series setting, where a high quantity (100,000s+) of series must be forecast simultaneously. This is where a modern technique like neural networks can truly shine vs. more traditional series-specific methods like ARIMA - we don't need to create a massive set of fine-tuned, series specific parameters.\n",
    "\n",
    "In this notebook I'll be using a dataset of daily wikipedia web page traffic, available [here on Kaggle](https://www.kaggle.com/c/web-traffic-time-series-forecasting/data). The corresponding competition called for forecasting 60 days into the future, but for this demonstration we'll simplify to forecasting only 14 days. However, we will use all of the series history available in \"train_1.csv\" for the encoding stage of the model. \n",
    "\n",
    "Our goal here is not to create an optimal model - check out later notebooks in this series for that. Instead, the focus is on showing a relatively simple implimentation of the core seq2seq architecture.\n",
    "\n",
    "Here's a section breakdown of this notebook -- enjoy!\n",
    "\n",
    "**1. Loading and Previewing the Data**   \n",
    "**2. Formatting the Data for Modeling**  \n",
    "**3. Building the Model - Training Architecture**  \n",
    "**4. Building the Model - Inference Architecture**  \n",
    "**5. Generating and Plotting Predictions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading and Previewing the Data \n",
    "\n",
    "First thing's first, let's load up the data and get a quick feel for it (reminder that the dataset is available [here](https://www.kaggle.com/c/web-traffic-time-series-forecasting/data)). \n",
    "\n",
    "Note that there are a good number of NaN values in the data that don't disambiguate missing from zero. For the sake of simplicity in this tutorial, we'll naively fill these with 0 later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "      <th>2015-07-08</th>\n",
       "      <th>2015-07-09</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-12-22</th>\n",
       "      <th>2016-12-23</th>\n",
       "      <th>2016-12-24</th>\n",
       "      <th>2016-12-25</th>\n",
       "      <th>2016-12-26</th>\n",
       "      <th>2016-12-27</th>\n",
       "      <th>2016-12-28</th>\n",
       "      <th>2016-12-29</th>\n",
       "      <th>2016-12-30</th>\n",
       "      <th>2016-12-31</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4minute_zh.wikipedia.org_all-access_spider</td>\n",
       "      <td>35.0</td>\n",
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       "                                                Page  2015-07-01  2015-07-02  \\\n",
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       "3         4minute_zh.wikipedia.org_all-access_spider        35.0        13.0   \n",
       "4  52_Hz_I_Love_You_zh.wikipedia.org_all-access_s...         NaN         NaN   \n",
       "\n",
       "   2015-07-03  2015-07-04  2015-07-05  2015-07-06  2015-07-07  2015-07-08  \\\n",
       "0         5.0        13.0        14.0         9.0         9.0        22.0   \n",
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       "3        10.0        94.0         4.0        26.0        14.0         9.0   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   2015-07-09     ...      2016-12-22  2016-12-23  2016-12-24  2016-12-25  \\\n",
       "0        26.0     ...            32.0        63.0        15.0        26.0   \n",
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       "4         NaN     ...            48.0         9.0        25.0        13.0   \n",
       "\n",
       "   2016-12-26  2016-12-27  2016-12-28  2016-12-29  2016-12-30  2016-12-31  \n",
       "0        14.0        20.0        22.0        19.0        18.0        20.0  \n",
       "1         9.0        30.0        52.0        45.0        26.0        20.0  \n",
       "2         4.0         4.0         6.0         3.0         4.0        17.0  \n",
       "3        16.0        11.0        17.0        19.0        10.0        11.0  \n",
       "4         3.0        11.0        27.0        13.0        36.0        10.0  \n",
       "\n",
       "[5 rows x 551 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "df = pd.read_csv('../data/train_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145063 entries, 0 to 145062\n",
      "Columns: 551 entries, Page to 2016-12-31\n",
      "dtypes: float64(550), object(1)\n",
      "memory usage: 609.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data ranges from 2015-07-01 to 2016-12-31\n"
     ]
    }
   ],
   "source": [
    "data_start_date = df.columns[1]\n",
    "data_end_date = df.columns[-1]\n",
    "print('Data ranges from %s to %s' % (data_start_date, data_end_date))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define a function that lets us visualize some random webpage series as below. For the sake of smoothing out the scale of traffic across different series, we apply a log1p transformation before plotting - i.e. take $\\log(1+x)$ for each value $x$ in a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T2yVx69Yt6tSpU2z5RWFubk5GRgYXL14kPDwcb29vufEaN26Mnp4eFy9e5NSpUwwePLhQ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QKhtF+f+9+h0TQAV6euj8XQZKXh7Vmbv1+Y8fFtFXr5SD5qNvnT0zMcX63wlEc2DEQVBlSFhUYL\ny1mfXAmY0S2m//zUH4CczgCgL7gM73esUN0fc7547VrbPYRjPKHymhEXVF1Ls2XbcXh0scj/OHu1\n4++D3gK3L5PgN4/u5PZcmnOlGgXN4NsLBS5WGmxLRHGAL0679OZk1V2ALpTr7E3HuXd8gJ/cN4rt\nOHxrvoDpOFypNDm+WuHBhSKfm1pek464b2blmha+hmnxQqHGrlSMhCJ1CNUdx+GLMyt8c77ArAcC\nfYZgualjOk5wOj/Sl8QBLhdrlFomKUXiYDbBiqYTkUSyETkoBjhVrDFT0wKw9pFLC3xrvsDX5/Lk\nX2T59t9emOfLs64H03Mh8NrSr42psh2Hlm0zHIswFo8Gm/k/1Wh6kz4RkXnrjiF+et9ox0asSiJ2\ny0JW5Q4/qF6gKsxiNi3X1+piuU7DdO9X3LM/qOgmUbG9RCR6gCo/7h7OMqBG+LGdw5x+5CrT3oKe\n80TWPlA5U6whAtsSKu86vJ1fPjhBv6pQNSwM2w42i/Onl5EFYcMqQ3DBwZ+engn+XVhHj/H4Uok/\nPjnNHxyf5Gq9hUC7EfVyU+evz8/xPu99/O+pWzZfu5qnaljcNZThjdsGaJgWp4quJiwIB4onVsiK\nUnCQm/MOKj+8c4if2DvGWCLKkvfYmVI9GJeKd3998CkK7f8Px0RCZSzeyWDFZIlDfQleKFSZrmn0\nRxXG4lEEQWB70t1ILpTrWI7LXH3vthwRUeBVQxlm6to1WSzYpjt/RDnW8V/LbHae8i0zSNGdvkY2\nrFf4c3251KTa1DuYVcP5x0v/WbbDHxy/zBPLpbaNwHqg6jpgKseyGKhfxYq6YzMfGn+vf/MB3v6z\ntxNVXRZz264+oqrMxdPLPd/rWsOvyNuK6W8Y6NiGgWHbHayo0GVK+akrS8HjKUVmPK4yXdP4zJUl\nPnBmtqds41rSzXZLQ4y1QZWUSoEgoC+6gF/8TrVU8DVVbVD10iZLNiITk8RrEii/UKhy3+xqoCvx\nF9buU+OdgxkcBz5wZoaveszNnYMZfmD7IDau5uFypcn9s6usagYHvNN7SpHpiypcqriAa66hcf/s\nKnFZYkXTg9J4cAHSY0sl7p9dJa/pW9IDPbRQpGXZvGF8gESXJ9DlajO4Fw6wPakyVW1S0c0AIH3X\nWD/ZiMz3bR9EFgSmyw1KukkmInMwm0AA3rZziD3pOLOerso/gZwt1XnCS4vM1DS+NV/gj05OUzNM\nvjSzwuemlra8gJd0gztyaXamYnx+ejlIaWrGtTFV/uKrSiI3ZRPM1DTKusH8y6ASsNY00K7R+qNh\ntlNTCUUiE1E6QE5EFDDLOlJMZiZUztyd/qsaJo+GwLpm2RxfrfDXF1wN4mAsQkKRgvTf4f5EsEio\nPUDV/kycXzgwHlSobVOjOJaD3rJ4x+4R7h52Dzg+U7XQ1BmJR4lKIlFJRBIF+iLua0stMwBVVtNk\ne1LlfLm+4fi/7M2nsXgUSWBdkevzhSo5VeFYLsWuVIzXjPQxXdNYarb48IW5DhGtX0n5wHyehxeL\nvHIow02ZBNsSKlHRXVdWNYOIKHBzFVafWMQo66QEkaVmC9txgpT6oWwSWRQYikVY0nxdYqsDkILr\nU5WJyAxEI0FFYDjePJHj5w+Mr/n73nSchmlzoVx3bTW8yEQU0oocHCaSisSxXJrfuW0Px3Iuk7HQ\naFHWDf7mwtym/QUts0FZGMTxRoMkeYJfq9mxITmmFRwAYP3q061G3QdVxSa1htHB+twopsp2HJ5Z\nKW/IulcNE82y+cL0SrvfXdcm79gOoihcF6G6Y1kcXXgAOeUCp3yrvZ5GojKDI6ng35IksufgEFMX\nVzFeYhV4qWVw2euvu6WDckf6z+DxpTIl3eR2b8yJqhSgDkUUuCmTaH8PSeTWgRQN0+K5fJWlpt6h\n81zzGVus/gun/wRJQkom0Rfc9U6KeUL165gqfXmAKuH6gipBEBiNR4OF7aGFAh84PbPh4rzUlSr8\nvolB3r5rmH3peMffh2IRXjvaR9OyuW0gxV2DaQ73Jbl7OMvv3Lab7902gGa5KYa7BtPcNZgJXptT\nlWCTsxxX4/DT+0ZJylKHmP1iuYGDm5r7o5PT3LdO6XmpZQRg5XKlwa5UjJF4lIQisdBoBZPgVKFK\nVBI52p9EEuDN29yUw4VynfOlOhlF5lVDGf7dLbvIRGRG41Gmyg1KukE2orA7Hec9t+3mUF+SbQmV\nhmmz2NS5XGlybCCFKokBwAp74bz3xBWeXC7x7GqFb86treT68swKT6+0dQgty0a3HXKqwo/uGgbg\nq1fd796LqZqra+umTrUQqNqXjuMAHzo/xwfPzv6jeng5jsPvfuhJfvmPH+KRFxY2f4EXfpVXPGQf\nEl6sBUFAW6hjaSaTdDJVhm3z7YUCX5pZ4WOXFgIALAsCLat9ktyXjrM9oZKQpcD4NhtReOvOIcA9\nrIQjIon87P5xdofmiK99a7ZMbhlIBWArEbru3V2Awn9OSTco6yaO7WDrNsdyaVY1Y0MNX8Oy2ZmK\n8c7D28lEFAo9NFXFlsHVeovbc2neunOYX7xpW5CqvG9mlZJu8tP7xnjnoQn2Z+LclE2wOxXjcqVJ\nRpH5vomc6yIuCIzGI8zXW+RbOjk1ghBaquIIQfXSXEMjG5GD7z0ci9Cy3HnTsm36o2sb675iMMNd\ng+me31P0Pr87tiXcU7huOwEY9GMiqQaHKV/T5gM8AVhq6lwqNzhfbvA/z83x7Gpl3XL2UkvnY8a9\nPOetUz5TZZvNjg3JMY2O4oeXYnFjOw6ad4BaKTWpNY0OpuNGaaomK00+M7W8ocFquG2Z7lej9WCq\nrgdL5b+3LUk0vZKF/Doa0flGi7+fXCQ7nsI0bWqVze+/abtZGn9v8g8G4EoCwDX9Xdb0TSvBw/fg\nfFXj63N5bsokAlAlRWUkz+rmLTuG+Il9ox2v35eJdxRm9Eodbpb+c2yb1rwLmmxNQ4iqHY9L6Qz6\nvNtpQ+4fcC0VvtPSfwGosl56+s+PfZk4840Wi40WZ0t15hotihuwAxXD7Ng0MhGZY7l0z1PGvWMD\n/PieUX545xBv3TkcaE3issTedJycqvCmbTneunO449Tp0/qyICDgVjtsT8bYlYoxXW1X1V0o14nL\nIr900zbG49FAgwHw1HKZx5dKNEyLPz87ywfPzJLXdJaaepAeiEsSJd3kg17z1aWmzmgswlt2DPFL\nN02wPamSjci8UKhxodzgYF+i43uOJ6JMlxsUW+174rMUEwn3Mx5fKmE5DrcOpLl3rD94ra9tecuO\nQV4z0se/OTjBHYMZThVrHbYTpZbBI0slPju1HAArPy2VVGSyUYVjuTRninValh1s1qIg0GgZLDRa\nfODMLH95rjNdCy7T52txVFkKGIZVzcB2tqbTuVGxWtYo1dyF6vnLW7cO8e9rXOkENmHQb5g29ZlO\nnVDLA/hfvZrnieUSU6F02qCqUNJNLleavGY4y88dGEeVJRKyxGKzhYM7D+4YzPDuozs7wFBDM/i1\n9z2ypsG1D3672US3FYt7Or13fKDjMd8yoOgxVZZn7npTOk5MEnkq1Osur+kdG3XNMAMWrD8qU2wZ\nOI7DE8slPnppgYZpBWyNr68Dl40SgUuVBmlFctNrCZWf3T9OXJa40zsM3TPaFxh0uq9TWWi2WG7q\n5FSlw6cq7g3vhYbOXL3FeKINcnz9m1/kko2sBVWvH+vn1SN9a/6+UQzFIkS8NWakC1SF2bBkiGVU\nRJH+qMJSU6eouw2vJ5Iqn76yxHtPXOnZMHtFBxCC6w9AldWZ/nNMs2OeT22SvgV3E+/FZDdbZmCL\nslxsUm0ajBjt8Xajqv+ueOvDiscsPrNSDg41PuCohlLoDyyWOXX0FVTEzrnZcGyq44lrakC+blgW\njUR7/K7XR/PB+QIn8lW+oNVZPdJPrb75Z58t1bhvdpWvXl3lC9PL/P+nZgIw46e5b+5PYjltkLVe\n+ODk9JG7+Hi+xXAswo/uHibjzXFJlZBUdyxmIzKSIPCO3SO8zTu4iYLA903keMP4wLqyjbZQvTcQ\nqjz2CNO/99uYpZInVO8EVXI647qxCwLK0KDXpub6pf/kzZ9y40O8zuk/cFNy35wv8OhSqUOI3ReR\newKlitdOo2KY2A5Bk9NeIYsCN/f3dsRNKjK/fmRnz8d8UJVTFXfQeIvuzlSMk8UaJd0kIoqcKdU5\nlE2wIxXjtlyaL82sUGoZtGybL0wvYwMPLxapmxayKPLhC/OYTvuk+uqRLKuazkJTZ6amsdTUOdqf\nIiZLTCTdAX0gmwgqmQ73dX6XiYQasBnZrhP1UCyCKBAI3ccSUXalY0iiwKVygzOlOlFJ5K7BTMd9\nfnK5zKliLdis/DSoKMAzKxXuHMwEOjDfrPBIf4onlsucK9UDoXo2FaGhmXzyQpvlKXtpSj/un3Vb\nHoDLVEmiwO50LJigU1WNg9lrczS+XjG77N63ZExhZRMvmRfyVZ5YKbM/HQ8KJxIRiUbI8/Fn9o8B\nXuWsaWPO10nvdzdmWRDQLItS02RIjfDje0eZqTU50p9itq7xlPebABwMjYGEIgWi7ZQH4vq6xkGh\n2qJc17m6Umfftmzw9zBT1R2/cWQnCUUKdER+pCMyouBagpR1A9tjIjXN5KZsIhgrjuPwd5cWKOkm\nv3XrLhRRpG5YJNNScI0v5Gt8fHIxGJ/FlsFAVCGjyAyEtEoRSWQ4FmGhqbM9GVuzJhzpT6JKYx0V\nluAeOB5fdijpJsfUCPOOQzTiutjLpo0gu6C90DKCNBu0K/r8NH9/9PosvaIgMJZw0/mjXVWDdw2m\n+dZ8gbppdcwP93oiLDVbRESBdETm5/ePc7JY5bNTyzyyWORtHlPsR9Fwf7PLlQa24yBKIaaqO/0n\nXxtT9b8uLtAXVdakN+sh7eR8vk5LtxhnlSkOADfGp8qw7aAQZ1UzmGu0+MzUMiuaQSYi86WZFd48\nkQvmoyqJPJqvw6u+B231Kvu999FMi4cSYOzL8OXZFW7KJuiPKuwPpbs2uw7dcgKm07EtGgl3PEUl\nkbOlOn9zYY6W7fALB8YRBYGmaXGuVGdfOk69ZTA/FGOx1mLCe0/bcXhwocAzKxX+9Z4Rtifd39A3\nkX0idHhZaLQ41JcM0tz7MnGYdf/ezYiGw7Hcrhcn7riHnTL89E3biEoiEW/OS1EpMBD015RbBlId\n73Gk3/23Ztk8ulTCsp0O26W2pUJvnNA4fw4cB6OQXyNUB5Ay7n2UBwYQlch3plB9LVP10kFVXJa4\ndSAVVMWAK4p774krvNDDN6qim6QiMvd4J0W/fPx6Rk51B1F/VOFgX5KMd1rd6Z0op6pNvjmfR7ds\n7hl1r2OHJzidrml8/WqeiCRypD9JSpH5sT2jfNdoX3Bq8U+q25MxfvHgNiQBnl4uo1n2Gm3YvWP9\n3DmY5pb+VPD5foRBVnfKRxZFhtQILdsmE5GJyxKSIPDKoWzw+QNRpWOTGotHSchSh+j4UqVBSpF4\n/Wg/V+saDdMKKHU/VbEjqZJWZB5bKtH00jp9qSg13WRR17Er7qnpal3jfKHGQ3N5HMfhUshpWvXA\n8Z2DGW7KJpjwNqB/rJhdriEAx/bnWCo1101JO47Dl2dXmao2eXK5TMOyEXDFyeHw00KW7eAAjuWQ\nbNoookBSkdAsm8VGi5F4hKFYhDsGM0Qlkb3peHBvFFFge6K98IS1WiM9KtGgzUhpXeDJB1W9igmy\nUWUNoPK/QzaiUGgZrGgGZtNrYt4wGI1HqRoWVcPkYqXBglfpd6pQw7QdmpYdgPBXDGWJSAKnCjXu\nHevnrTuGmPeY6m3JtRvBuPed/TnWfU0Hsok1Kbcw+7QnHce2HVRFQpYEDN1mQFWCqsLR0L1LKBIp\nRWLamwPdIPWlxJ5UjLQik42unavvPrqTXz44sWbcDMej5DWDVc1wGQNR4NaBNLcNpDiRr64xDy5b\n7ns3LZv5egtBjIAgYpuNTqbKajNV2xMqc5v42q1qOvmW0dM6o+mNoaFsjGX/AB4N/SUAACAASURB\nVOKNWUm4/kzV2WKN33v2ciApWNX0oBvDE8ulwEvtdLFGVbcQgN++bTe/uWuAsdlJ5hLtw8WjSyUM\nEaI1g8lKky/NrPDQFqrnwJV9/OW5q7zv9EyQknMsi0bCBRu+n9P5coOpqgvgq4bJ56eXMR2HN4wP\n8KZRlw3Oh+wJnlwu8425Ak3T5n9dXKDudQnxC6puG0jxY3tGyChyUPCRb+kMqBEG1QgxSQw0jODu\nmZbt8LcX53nWl3FYFpoax4hE2SdaATkhiQIxQUBUJaSYBI4T2IOsF6PxKJbjrGHHAvPPddglbfKy\neymViguqYp37m18BGBlyDw6CLH/naqrM61D9F467h7MBfex/hiDAxycXebhrgFcMi7Qi84bxAX7r\nll3Bxn49w/cA6tZTDMfcAXuuXOeZ1Qq35VLByXYkHiUiCkxWG1yqNLhlIMWP7Rnl3x6a4FBfktty\naUTB/X6DXSX2O5IxTngLfDeoSioyP7xzmHfsGVnT0Doiidwx6i4Q3aAK2kLe7hOLn8bp/n6CIDAS\njwTNYluWzcVygz3pOAcyCRxcHZnfxsRPVYiCwJsmBpita0xhIUsCw/1xVhwLBIHSpRKiAGeWK/yP\nZ67wlfkCp4s1yiFq3gcON2UT/PS+MfakY1yta3x5ZsXtldgy+MtzV/l/n5tc19vresbMUpXh/jjb\nBpO0dItKo5PGP1+qM1NruhuNYTIQVSgbJnlNJyqJPXU10Nn2IZ3X+d1je1AlkbJuUtRNRmJrQYV/\nb7Yl1I6ToA+q+qLyGqbSD79woNm1Gfpga6sGrX4MxyJMVprUTQuz5t6TalMPxthio8WTy2VSisRA\nVOHx5VLweyU8Y7+xeJT/++YdvOvwdu4dH+Dm/iQCbjuYbYm1wMmvkOs+VGx8nVF+dv8Y7z66k12p\nGLYnRo5FFRot9z77VhTD66TjIqIQ3PvrEa8f6+fXjuzoOTYiksi2HqBxJBbBwfUHCwO8Y7m0W50c\nOng4jk3ZVkmI7ve6Umu6GjMphtVtqWCaQap6byZO1Whbc/QKP50YtDCy2+2o6l7qaUdYhO0dROOy\ntIapmm+0NjRaNmybE/kKZ0u1NUCvopt88spSsBYOqRHynhZPFFxWbG86zutG+5itaSw0WyQV90CZ\nlgVG5qcoxpLBmHxyucyg7jBwtY7pONiOe33+5zqOw8cvL/CpycWOa1nVdP7+8iJX6y0qhsmHL8zx\njbm8l/5z78Nbdgzx747u5F2HtwNukdIfvTDF6WKN1432MZ6IMpp2f/OCNz99XeXOpMr/cWCcmmlx\nulhjsalTNSxuHUjxo7tHONKfYkBVgrTfqmaQiyqIgsD+TIILZZeptByH//L8Ff70tJsq/PTUssti\nWhbVjEsIZJ3ONSAuishxBSkqI1qdpt+9wp/73T0rAwDUA1Rb1SrG0hIAZrmE0+rBVKVdpkoZHgGg\npCawv9Oq/wJLhevIVIG7AO7zTuQ/uXeUVw5l+M2jOzmUTfCVUGuZlmXTsmzSEQlBcOnwGxEuM9PH\nbblOMaooCBzIJDhZqGHYDkf7Q4uIILA7Hee51Sq67bArGet6T5lb+1PsSKprqoZeNdw+OXUbCm4W\nP390Jz+1d7Qn1RuAqq739DfgXiLckViUpaaO7Tg8MJdHs2xeNZRlPBElLkucLdWoGe7pLyxqvnUg\nze5UjLLoplmKQ1HUfVlsw0IvtuiXZU4tV4ikXdD4d13eOKrUeUJ/7UgfB7MJHlkqUdRNzhRrXKk2\nqZlWUOVyI2N2ucbEUJIhr/Fpdwrwby7O8+dnrwYpL98e5HKlucYnKhxhUKXrFpIgEJXEgBkZia9l\nnPwNpBtw+Ev87lSc9aINnjo3zICpukZQNZ5Qg4rVAFR5TBW4JruXKg0O9SV5/Vg/V+std7OBjt6f\nMVkKGNO47Gqlen1HcMfWz+0fCxirrcb+TCIAIn5qIhGT0Vpm8NkRUVhzIPFTP7rtXJeKMD/83/pa\nIqy36gvpu3ybjnB1mW02KTtJxqJWh6WKJMexzSZNRNeGZd/NFFtGsK7u9fR+PttztlRjvq7xzfl8\nYJfip54aptuq6C8+f5r/9rHncBwnYDt3jobWQ4+Ni0liB6g6U6zxgdMzfGJysafZJMDTKxX+fnKJ\nj1xcCPy6/HhsqUTLsvnVm7fzm0d2cvdwFsN2OFuqsTsV55cPTfBT+0Y50p/CAS6U6kFq3LFtRhZc\nO47n8lVqhknNtMjqDsmaEcwzzbIptAyu1jQeXCjyQqHG8Xw1EP8DfHJyibOlOq8eznJrf4rpmsY3\n5wuUYimqqSwR2/WLy0YVhmIRFNH1KYvJEr968w6+d5tbVJGIKoiGTdmyWW7q/OHzU1QMi3vHBxhP\nROmLyJwr1wO9V5iBHVAV8i1XlF5qGQx4GZYD2Th102K+3gp+4zCL9KHzc/x3O82J2+8BIGt1Hhgz\nooScUlASCuIWvCgHVAVZENaCqh6WCo63r7zv/Bxa1B3bxorLLK7RVGU8pmp4hNmaxt/uvZ2zuw9v\nej1bjZeFpup6WyqE40d2DVM1TMYTamBvcPdwljOlOtO1JjuTMc57E3szOvKlhiAIvNGrvOuOg30J\nThSqKKKwptz6SF8yEA72OlV36x/8ONyX5J6RPqZrzQ6gspVQJLFDZxMOXxA/lugEVTlVQYA1vcvA\nZdwM2+FypcFjyyVuz6WZ8E7QN/cleC5f5XCfEKQTwzGgKkwLEN+RpohNc6FOK++1ZrAFVlUJAdCW\nG6RGEmQjcuCg3s0IxGSJe0b7OFOqs9RoUTddIBcRRSarjTX5/esZjuNQqLR4xaEYQ55R31Kxwd5t\nmeBxP741XyClSBztT/H56RWvEm/98RkGVb7+TJXEwNW6F1O16lH83SzmzX1JLlcafO+2gTWv8WOz\n9N+1MlXbQmPJCIGquCyRjcg8vlTCsB23Oi+T4IV8lePeZpTcYGwf7k+y2OwUjfshiwL7tqhxWS8c\nx2Wq4lGFZsti1AOvw7HoGuZob3p9kPoPHUnFTd03TCtoPA7uISQhSx3VZZbZoEqSfRGIiipz9RaW\n7fCN1hH2OgW+ctcb2Zsa5OyRuzCbDoJqE5VExhNRRAE+fnmRN03kgvSZiHsYPNSXZLLaJCaJNC2b\noqZz4lIe07I5N10MgPnO4facFBV3g4+KYkf672K5QUQSkQWBT08tkZQlYpMK/2rHUAA4TxVrZCMy\nZd3kQrnORFKl2DL4u0sLrGg6h/uSbd2r7n5Ow7QZT0QDUD4ai5CNyJR0sw3mbZuBlQUipsH9s6tM\negci1XQwHYG37BhEs2y+PLvKfKPFl2dWKRsm/VHXFuWBuTy3DqSomxZX6xr3jvfz3WMD2I7DG7cN\n8Ecnpziz72bmxnYxrrUBmCQIjMWjTNc0bs+l1/ibRVs2NcW1C2qYFj+3f4wd8SjnXljkQCbBs/kK\nw6pbCRpt2Tzx5GVue+V2ctEIDdPmal3Dpp1h2Zd2rXXOl+sdmuK+qMy7Dm/ndKHGA1fmmZ/Y7ab3\nzE4wlEZElETETAShsLndkeRlOLqtkWzLxOyq2HsuX+WB+QIgceHwMW459TTGsstYrQFVfW5hVWR0\nNCg4uLzzwKbXs9V4WTBVay0Vrp8AMR2R15xEJ5IqkiDwzEqFD5yZ5ROTi8Fz/7FifyaBLAjsScfX\n6E4O9rmPDUSVnte4Xrk1wJsmcvyfByd6PvZiY1cqxk/vG+VgtnNDykQU3nl4e09g4mtzPju17LrM\nj7crBo/2pzBshxP5as8NMhNRsCQBKaeyJxWjfr5Ea6mBJApYy82goWj5XJF3H9rOuw7vCF7bi2L2\nQd9SU6dmWKQUiV2pGE+vVHh6pbxpl/QXG5ruGm0mVIVcRkUQ3FLx4PHQYaJqWLxiKNuhhbl9nXJ7\n6DyI+KDK30wyirxGqAzwmuEsKUXipq7fMR2R+al9YxumwAPwpPdmqkzL6dkLb73wN624JOIY7nep\nejYnu1KxgMXanYojCAK3hsZYtylpOF49nOU3ju5cw1her7BsB1EQiMfkIP0HvV3R3WbIad6+ziHo\nHyKsWo3C/ffhOA5DajuVFo6cqgSA27RtHlnRMJHpU1zwW2i5zbDPGsPcVz+AISucvflOAIqWQ9O0\niEkiiijyA9sHickix0NGvjZQNkwemMtjOU6wXpyeLWJaNpIo8JWnZoP038RwCkFwjSO1lLuZK3QK\n1Uu6W5Dw3WP9FFsmlgNn81UeWXRlHlXDZLra5FguzXgiGjDBk5UGc40Wuu3w2lDl5baEyr50HEkQ\nOipsBUEI5kubqXKQbJt3PP11htQIFwNQZSOKAncMZnjlUAZJIJAn3NyX5OcPjPOq4QxF3WSq2uR8\nqY4D3OQV0YiCQDaqcKQvxbl9R6mls+yudDYN9+fNbT3W3JjlUBdhtqYxHIuwL5Pg3POLPHj/edTV\nJobt8JzXYupjf/4kzz0xy5WL+YCZ8lOp/v6Z8Lo5nC/XOzR3e1JxVEni9sEMhyz3sJuoVZCMTqYq\nSXsttkpbq4gci6tcrWvols2pQpWPXprnxN4jfPIn3kkttObN1DRUSWSisMS5I6+guGs/n9t5FD0S\n7fCpAojtP8Dov30n8YOHAs1aOX1tVbcbxcuDqbrOPlWbhSKK5FSFM6V6hyD9RjNVG0VUEvnJfaM9\nU2eqJPE94/1rxKb/WOEuLL1ZrPUqQ3yquqSb3DmYDkT64LJvfRGZom6uYakgpOuKSowlVHaMJGlo\nJqIg8OypJdQ9GfqG4jiGTbNlkYxF+PUjO4LeUt2hSi77sdh0F9OEIrM7HeNcuc5np5bJROQtV+lc\nS/jsTVyVkSWR/lS0A1T5jUW/fyLHzf2pAAj9wPZBGqbFK0KeZ93hzx1FFml57+N7ytwykOqZbtqR\nivEfbt39or6Lto52qttPLJNcf8x+9OsX2D6c5LVHx4jLEjlVIYbIpPd4rekuym/ZMeSyiZ5hKLgi\ncT82An+iIASb340I20v/xaMKlWqLbETmWC7VAfrC0YtV/qsvnyWbjPC2e/bcsOusNQ2eu7DC0dok\nq5/+JMljt/O2XcN8cXplDTOeUyNcKNdpmBYfvjDH1XqLQfLsSaRpeaf+b8y7m7uNiGhb2KL7Oxdt\nB0w7AGqvHMoyW9M6Ulx9UZm6YfHIUgkBlxl9YrnM2bkKEUXknqNjfOu5OYayMURBIKHK9KdUzIk4\nUyPuvBRsp8OnqtgyyakKrxrO8oqhDKIg8KmZFR5eLPLakT6+NV/A8T7LdhweWiiimRYrmoEkwO/f\nvrdj7YlIIj93YBzHWZuqPZhN8MRymZS/Lvk9Ixs1tiWigWFl1ICmd6iTRZFtCTWoSv2usX76owpJ\nWSIqrnA8X6Fp2mQUuaPAAeB7JwY4vbiKLYrsKnW6pN8z2seedKyjstWPpCNQUESmas32ePS+ijZV\nRtgRo2JYjMnt+VEuNNizzz3wPu/5G/oFVuCmAB+YKwTf8btG+7kjdNjbZTZ5TE6SqhRxMp1zX3Xc\nij1RENCLW/Mvu2UgxVMrZU4Wa9w3s4Jm2cgHbsOUFR7UDd7hPW9Z0xlSI+w98QSz3/0WHj32WlbS\n/UzvOsDOEFNlOQ4vFGrccux2BEEIPAtbsTimbXfYp7zYeFkwVRnvR7vemqqNwves+cm9Y9zpDYpe\np/l/yNifSfRsUQFwz2h/YEfwTzEUUeRXDm/nFw6M8/0Tgx2PiYLAO/a4osFUD6YqnPYaVBV+6o0H\n+PnvP8iu0TS6aVM5X+T1noeLr8PIqRFu7l8/ledrvOqGRVKWuGswww97Xin+wldoGfz5mVlK63jC\nbBY1w+xgvRqaSfpAH9/Q6zy0UKAvrVKotIGf31h0LKF2jMW7h7N8z/jAhjocf+6k4koAePxGurfl\nrn9K02ekup3hdaM9dzfTVT19bpkXLrdP3j+xd5S7M+1rrXoi/ogk8vbdI/zQjqHgsTBjG9lE8Hoj\nw/bTfx5TJQgCb9810mGIulmcmy523IcbEY+8sMBf33+Okid1cEyTnBrh5w6MrzmsDUTdtkF/dX6O\nxYbOOyZUfkT+Gv0RkYmESkqRWNUMdkcbHBCu8PoTjwavLTkiDcvqSLuHWbvbc2m+dzzHW3cOERVd\nB21/rC/XNHYOp7j9wCCW7fD0+WXiqmuBM9QXI9ofyjh4prbgpmCLuhHo3HzW/s6xPnTb4cnlMk8s\nl3n1sFuhvDcdxwYmq01WNJ2BaKTnYQ7oOed2peIc7kuy3/uNnVDvP1+7qkoikuV0NFN+5ZBbOKV6\ndh5AUM19qlDjYtnVDHZ/ZiaicM83P8+xpx8k1uz0bUop8roH3AHB/Q0M2wmqe01vfubnKsE1JJx2\nEVep0GDA02tpls14vDON7RcWnVh1QfIrhzMdhQ6jRgO1UaOvsIJjdq6bjg2tYouE7qC1tsZi70yq\nDKoKTy+XA8LBlBXSpTzPO3Kg11tu6gzYOiNTFxAdh5W0Cwyv7DmEE1V5oVClaVqcLdb45JUlLvpa\nvhDWmNuCp9pW4mUBql6/3d1kw9V/W2nN8lLidaP9/PtbdrEzFeOtO4Z4z227O5qV/ktc/xhQI+xO\nx3ve5+3JGO86vJ0f3rn2JB82ShxUI2wfTrFnLMPbX98+2Q97GqWtCqSHYxFWNJ2ybpJQJCKSyJ2D\nGW4bSHG66JbrnyrUmKlrQTuirUS14RoGNgyL9x6f5EsX2sL5qmYQ35bExBXN9qdVCtX2RPaZqvQ1\n6t+gzVRlEq6Pl+M4vG3nMD+4ffCaixS2EusJ1a+lnZCmm9Sb7YV3OBYlGpr2tebGYPaVQ5mg192L\njYV8nf/y0ePXrAHzI0j/ReWOpt/XEnXNZLm4vr3G9Yj5VXczLnnFGB39+rrCT//MN1q8fdcwB9M+\ngBWISCI/s2+MvqjMsVSL75KeYM/cRe597iEOP/84LQTymtEB1PzxJwsCP7xziKMDKW4dSPM7x3bz\ntl3DAdOo4+AMxZgSLKKKRKWuE1dljq9WSO7JIEsiqWadux79GoJlo1muuL1h2hi2s0ZzOOix5ify\nFQTgjZ5GcHsyRkQUuFhpsKLpDMauzd5CFgV+Yu8oO3yGz2nbHgx7mrq+qOK2qQmNzZv7kmQjMrtT\nsY6/35ZLo9sOpuNwtIf/oeM4TExf5Ohzj2Hr66fNHNvGrLbTrNsUGdXTnvrX2vLbfjVNhj2GKuYB\nrdGJLKWCW9l5+4BLNEx0SWdG41FkQWDWAzPxrrS6YNv84Gf+ituefjBoqOyHZdmUnl9lry2vOYw5\nptlzTAqCwM19rq9e0GzdMnnDfR8jjtvTtmaYrjawUiRi6GzzeiBGtQYL47s47ih8/PIi//nEZODL\n5+u0wt01Vq+hV/BG8bJAEYFQPYQarRtg7BYOWRSCE5IgCC+b1No/5xiNR3tqxtIRKdhwwkxeOhHh\nvb/0St7zM3cQ9yZSY532Dd0xHI9gOy6QCbtNH+5Lolk2s3UtyLdfKNf5yMX5nl464ag2dN79wcf4\n9vPznF+p4ogCFwrtk2Xe0wgNRRTyLYN4JkKx2m4J4YsmX0y6yp87fSkVy3ZotkyGYpGOCtDrGa1N\nLBVgY7G6bTvoht1h8Aige+AwGVM63qtX/NCOId7plZW/2Lh4tcyF2VIAOq41fEuFRExxHcCvERhZ\ntk2zZaL1sNe4njHnfb9KdXNQtT2pklMV/vXuEY4OpHD8elAPB4wlVN59dBf7U54xpWSxL7/A8ILb\n3aBuWh2yCl/DmFOVDjAhCUJQuaiIAqYIzZTMY8sljhwboW8ixb3Hxvna1TxzgoUtCRyen+TQqafJ\nGBaG7TBZbVD0Pey6pBM5D+AsNHWyETnQqspeMdCFUp1Cy+iwonkxEa5G8zV1fRHZbVMTYqokUeDf\nHJzgR7pSwDuTKv2eQe1ED/uLDssKY/0xsvLxv2Py134FW/MATyxC7vk8v7R3NFg3W6H5JnticaVp\noUQkBoeTlD1wf2suRU5V1ugtRUGgX1UCxq1bs+pYFol6lYihY3eDKt9QOB7xNJft/X75Y/+Luff/\nac/vNRx37T/qpsWrh7P80H0fJVUr80q7wUxNC2w5+uquV9b+PpftPnr8URxR5KmWe422Ay8UfFDl\n3qOGYZGw3XtSu8Z+rOvFywJU+RG+yf8QKcB/avG1p2Z48Lm5f+zL+AcPWXTFy6LtrKliHO6Ps2s0\nHYCq1bLGcmlza4QwexMWx/veRVdrWmCA17RszpbqPBnqU9grrixU0E2bR19Y4LLvv0N7HBe8MvVj\nfa5ksxWXMS2Hh+cKXK40qOgmqiS+KMbUnzv9Kfd7VW/gBg1tTVU3ja8bVrBx9jIA7X59vQsE6x5Y\nc9OYN943zBfDVzdhxdYL23E1VbGojGU7ASjcatSb7e+4XGxs8MwXH47jMJ/3QZW7mWwEqjIRhV8/\nspOjvg7HA4pC13Yhyl6KU3YQVZVUpe39lwzpdLIRmagobghekoqErYiYkpuumkuKZA72M7FvwO1y\n4T2vr+l1JGjqREWR5wvVoFKx21MtochBGrJbVrE3Haeou90z1pNcbDk8sOBYbkVgTlWYSKpBajgc\n6Yi85gAvCAI/tmeEH9872rPgqMM6YAOmqvK4m4b12axkOorgQKLVBvotzSSdVRmdyNA8u8qrhjLE\nKwbxRITsQBzLtKlVWqS8ziA7elSb5/x+nr2IiA0AoBkCVdApHTDyecx877Zd4fTxSDxKNu/qynZ6\novjHPSf4/koJIRLh7tF+3lRb4sCZ44iOTdGw2JlUSQvt8RswVZZFxjaRDX1LPoWXK42gq8B68TIG\nVTeWqfqnGA+emOexU4ubP/E7MKymScxeP80T9yb6J755ifd+5NkOxuC5iyt88HOnOv42qEYCf7RE\niBlKKjJ9UZnZuguqdqdi/JuD29iXjnN8Zf2GswBTC67O4PJ8hStezl6X2s7PZQ9I7EzH2JuOsyBY\nCIrI1xYKfH0uT8WwUAXhRbEmfuq83zP9ux6gKl/W+MsvnumZhvOZqpZhdfS/0wyLTNJdBDdiqvzX\nh0EFtNeAdDzSkUq8UeF/t9qLvF+27SAKkPBSSL7FxLnpIpUt9F0L39ulwo3xSStWWwHrV61tDqrW\nhrcud234F676Luc2YjRKstruy3dzKhb8loKnmfzuUMVvd6RkGTEdCT7Dwa2G/eL0MqokBnVjfQ13\njhmawcFsguOr1aB6e36usuZ9fR1ON3C6LZcOrGF6eZhdS4Q1VYIg8Ks37+CekT6cLqZqoxhPqL1Z\nKjpBVTf7Ew4fTPlaptywm0pcWWoXCeiaSVRV2HdoiOpKg7tjcfSaTiwRIeN55xU2WX98UXwvmx7/\nWoVodI2mygrpPoHOdLllrXm+H7loBL+Pdl+kbaWQs3Riksh8o8VILIJaLSElEqiyxN23H2X4h97C\neML9TuMJNUh3mnWDYst1/m+YNjEc1Ga9o58jwBOnFymH5nDTtPjQ+Tk+dH5uQ3f8lzGo+hemKhyO\n41CoapvqTK41nj2/zK/+6cObplq6wzBtvvzE9D8Im2A7DqXTBfZY6w9XNSoFa365rrNabufH3/fp\nkzxzbrlD/yOLArmouzgku05cEwk1AFX9UYXtyRh3DKYpGyaTGxiETi1WgwVjVTfd3lSCwONLZSq6\nSdXL3w8lVF41nEVzHDIH+3FwmbHlpk6x2OST37p0TfcHQkxV2meqXloTV8u2+f2/forHTy9ybrpI\ns2XyZ587RbHqueKHfvfwGGgZFlmvJcxGmir/NS3D6pj3Le8epRKRa9IomZa9hhlzLAurur7DNrTB\n54udV5aX/ot7OqRGy8S0bP7oEyf4xrNrm313R5ipWy65QPzhF+b59onrx0j7IH3PWDoAVb5g/cpC\nhV9//yOUaxv4BgUHiTZAqNR1PvGge42O4iBGVRTT4A3NVX7l8AR/+JHjfOXJ6eD5N2UTG2r7dqdj\niF7K8PZcmmO5FLIgUNRN7hnpYzjmbqxpzb3ulqbzpokcP7h9kDtyafpM+LPPnOwYS9BOCYYr2MC1\nkfi/Dk3wntt29/TVu6YIaarATWsKguAB7utQRLFFpiroiedrM7MxlIjE6lIteEpLM4mqMhO7XIC7\nOFehUddJJCMMjaZRIhKXz61seDkDGzBV/j0QI9G1miqvUjbm/RbhQ9d6mipw06Y+y5mNyO37YVmB\nb+Odgxnseh0x7qYr5XSage//wYBpG09EGXZE9HKL+rQLvucbLRqmRUxwiDXqQWEPwGq5yf/44hl+\n7X2PBBXa4cbYlyrrs8ovK1AVBlLmNdLo3+lR10x0w77uoGpqsUq1YbBavrZT8lefmuFTD17m4ecX\nNn9yVxSrrWvyMNINC6tpktmkdD4eMjGcXnQ303wIXJ2dLvLVp2aCf/ui0tXVzgkykVAp6yZ10wpO\nugcyCRRR4GypxnoxtVjh5l39jA4nkeIy2rJ7T79ydZU/OTXNimBjtyxURWJ/Js5gVEEdjCHgcgEr\nmk6z1GK1orFUbHRc+2bR1lR5oOoljpOz08VA77Ra1phaqPD0uWVOXXFF+2EWKQx+dN0ik4ggCsKW\n0n/QqYMzDJ+pUtA9X6+NwjAtak2D+x6f5j0ferKDNSs/8hBXfvu3MAyTpXVSa/58qjZfHAj1039+\n+lnTLaoNA8t2tgRsezFVX396lq88OdPz+TNLVU5c7J0mWS8WCu53v+eWMc/7HO5/7DKmZfOpBy9T\nqulcuLp+atvXVIULAqoNnYbhzTcFBNUdd7c0iiQFiXJNDz53K3HIc++XHHjbziHevmskaEL86pEs\nx3JpDvclEWyPIW3ppCMyrxrO8rZdw8QWNBxn7WHCb1zdK/V43bS0dieo8uNamKqNwrG3CKr853iM\njyAI5IaSXaDKIKrKpDIqakxmeaFCo6YTT0RQIhJ7Dw5x+dwy+gYHIr+QYV1QJUmIsRh2s3NP8UGV\nv06H1wDHMgMw2CuGY1FEIC2176dj2xzMJoI+v3a9jpTo1IDtz7oejzuSYX4nAgAAIABJREFUMSTD\nofDMcmAcvdTUPVAFsWaNaghz+IdHgP/++ZP87fk5nvW81g5mE4FQv1e8rEBVB1N1g4Xq/9SiUHF/\nxLpm9NxoLs+Xee5i+4Txpcem+E9/8/Sm71vyTqjhQbSV8EvATdvmY9+42EGTggswuk+N4G5Cv/uh\nJ7nv8ek1j60XPoumblIVF170pzxQ9fS5tq/L5x6Z5BPfvEStaaDpJlLLvb6//uKZDgBzMBt2C3YX\nkIgksicd51ypjuM4lGotfvmPv82lOXczKtd1SjWdHSNptu9yjeSaC3WsgsZ3jfYTFUVaEuCBBlEQ\n+Jn9Y1iayYDu+pQlZYnqZJlCReM//MUTvPvPHtsyY9vWVPnpv5fGVIXb56yUmsHv65/aWnr7usIn\nzpZhEY1IJGLyGhF6OMJO7LXQ83QPbKc9caphbPz9P/fwFf7zR55lIV+nWG0xs9xmpsxSCbtR55Hn\nZnnP/3yqZxFDoKl6Cek/QRRIhJiqstcCpb4FYOuDqtGBOEvFBo7jsFxsslxsBinScHzpsSk+8rXz\n13SN+bJGRBF5zdFRfuAV2wAoFhtcWaiw4GmtNmSqezBVtaZBMwSqxKg77hzDCOZSubb1MRgXRFoF\njT5RDObxj+4e5p2HJlBEkdeM9PGv94wGAEbv+i190Ny9Dg15m/FLZqM2CGcdUNVLU/Wi3t9/X0HA\nNrYAqkIMUW44SX65Fhw2fKZKEAQGR1IsXC2jt0xiCff+HDgygmnYzF4prPv+uQ1AFbaFIElIqdQa\nltiyHCRJIKb2AFXm+uk/cG1lvm/7IGK4359lccdghv946y5isoRVryMlOqsn96bjvOfYbvqi7cIX\nW7eRHbd9kmE7xD2mqh5aa0ve2P1X9+7F2pPmXKXBM6sVFNE1Hu7uOxmOlxeo+hemat3wQY/j9Naq\nfOGRKf7mK+3FdnqxytRCdVNGyB88hR6g6uxUgV/6rw8GwMuPZsvkyoKL2k9ezvP1Z2Y5fqEN6Bby\ndf7gw8/wzeNr0x+Vuk5dM5lcqDC9WKWyhc3fZ0WikY1BVfjU/+Unpvnkty5xcjIfvG5uxd1A8mWN\njz9wia/dfxFloYHdsjqYjH5V4bVez72PfP40qx6QOJhNUNRNlpo6s8s1NN0KGLFFb3MazyWI9kdx\nLBu91KJ0Ms/3jPdz55DrMRZeZPvVCMqFCsw1+PE9I9ybTeOYTkea8pGTW2MCfVAVV2WiivSSNVWr\nZQ1ZEtgxnGKl1AzGyUrJ3TBbhknS0xGFXdU1wyKqiMRVhXrT4P2fOdmTWQkzXWHwoRs2ApAMdBcb\np5cX8g2WC43g+s5Nt3U9fpqgUGpgWjZLxbVsbJD+e5H3y7IdJKHNVDU1MwATG4FKP3xN2e7RNMtF\n9z7rpo0DAeAJR77Suma2Ol/WGEirbhNkDyBJjs3Fq+XgvnXP8c7wq//a20WtaWDaEpYjIUQFxEgE\nZXCIxvlz5L0DYDfA2SiaukXp+VVeEWszDYooonZt3D6AabXcOfjQ8/PYHhAF1ujYbh1I8Ss377ix\n3TJCmqrOa72+oEqMxXBamx9+w4xPbjiJadhBVZ8PqgCGRtOUPXY07oGq/px7/6sbsOQpRebYQCpo\n+9Z9rT6oMrtBlW0jiSKxqMzB6hWsk892vG4jnd9EUuXu4WynaN+/Lx4It+o1xMRafzi/6lMzLCKy\nSDwqEzHbTcNjokCsWadhtysU/fmQ3ZZCikrI3t+H1Mgas9zueHmBqg6m6l9AVTjCoKfXBrBcbFCp\n6wFIKdd1HOjQFvUKf/D4TFg4/u4bFzEtm8n5TgHolYVKMPimAlDRBiVPn3XZoVOTa/2d/AV3ZrHK\nez/6LF98dGrD64P2KTq6RauB/RMuILr/yRnOzRR5zc2jHY+vlpsUKhq2YTN7xr3G5VKzw938Tdty\n3C2pFJYbnJ12RYn+ZJpvtAKg5d8/P9Ux3B+jLDhQN9kzmsYwbXTD5nbPgFPqGta7R9JMLVTYm47j\nNNcuKuHCBMO0erIXj59e5O89HZYii6TiSgdT5ThOUFW31Vj1NuKhvhgrpWawWfmbV0u3yHqC9DDr\npBsWUUUmqcoUKhrHL6xw4tJajUb4lBrWFemmhaKIqB4QDoOvWtNYw6gWay0cYG7FTXGcm2kLSP1F\nt9bovPZw+GnSF5tWt206NFVN3QzARHdlY6+oawaSKLB9OIWmWwHzCe1DQDgKVc0bU1v/PVcrGgMZ\nTwTt3ZOEAt8KHXo2AlWO4w/aTqYKwLAUUCWQJNKvfg3Nc2cpX3UPAhvqtLpC000c2yEZ3WSOe9ei\nt0x+76+e4sP3n2O11Az2ju5DmiQIN5Slci9pHabqOqX//N9MiiewW61NbTvCjI8vVl9dqmKaNrbt\nEPXG6tBY22g37s3lSFRCiUjUNshciILA23eP9OxD66f/XKaqc99op/8Ubi+fQzn+WOiaXU2Vfy9r\nTaNjPe6+F0AbzPr/bDTWpP/C0dJdFj2bioJmUfIObHFRINZw1w9fV1WqtZBEgUu1Jhg2sVV3XA3H\nIiQVmR/3zKp7xcsKVIXZKdP8l/RfOMKgp3sDsG0nAE/+QuynIHptJOEoeZOnm6mybKftbeNtEpW6\nzv/80pmApUrG2u7diyH9hJ9yu3C1vIYp81MDlYaBbthbqnQLGgRvwlT58W/fcpj/+JO3I0sijgNH\n9w6QTrQX1tWyFjALfnz58Wn+w188EVyfIAjUvFYKU171TF9EQRRc7dOK9zx/k18qNFBkEScisazp\nvOHgKK+9ZQxwf69MREGZrNJX6Fz0d42mqDXdlEm3hmrbYIKFVTfd6DgOv/7+R/n37394zff9yy+e\nCf5fEgUPVLXHyNefnuU3P/hYz3TserFa1shlVAazMVbLGkVvg/QXOi0kSPeZqmfPr9BsWUQjIomY\nwoIHtHuNwQ5Q1Qyn/2wishQA6LBlw6+//xF+4wNt925ogwGfFTo/WwpSpv4m12j4c6FT42OYVrsq\n7iVYKoghTVWzZQVgoruysVfUmgYJVWa43z1hnwwdRLpBlWnZVDxm6VpAYL6skfOqQoPKqaRCvtJy\nP7svtkn6f62myv/8lqkgqCKCLJO++9UgCAinXAairplbHnNbneN+n898sX1vTk62U1Vbqbjcali2\nzae/fXnz93TWB1XXQ6jueONZjMfBcXrqqsIsT7hCsC+XQBQFVpdqgUeVz1RN7OrndW/az93fvYdt\nO1zJgiAIJFJR6tcoBwnCY6rkVBqrVmtXRtKZ/hMdB9s0OXFplWfOLbdF9pZ7jZ/59mX+5JPP97gX\na5kqcCsfHV1fk/4Lh6abRBWJvmQkaNoOMCYLxBruePrD568wV9coVXWymSgXyg0Smk11qY4qiYHw\nfaNuHS8rUNWR/vtnWP333MUVjl9Y6RDb+hFuZ9K9oBYqWsAcXV2p4ThOhwZG002WS02eObfMf/3Y\ncwHbYZhWsBl1L6pnr7QXdx+w/d03LvDYqUW++tQskiiwc6Q9sBY9k8v7Hp9ibrXOLXsGMEx7jQA2\n/D1ga948rS2m/47ucR2TM8koe7dluPOmQRRZZP9ENhBw+9+nW++xWtawHYerK21Rpw8UfasESRTo\njyqsanoHU3W+VGch32C4L8bX5/LIosAduXSQHvN/L62okexKQ+wcdZ2LHz+9yNRitcNM79j+Qeqa\ny3w8cXqJumZyuYeg2K84BHdRTMYiVJsG04tVPvfwJA+emKfWNHqmk9aLvMduDGZdM1EfSNeaBr/7\noSfRDTuwbyjVWpRqLT7w2ZOA6yuWUOWg+q9X2i2c1utgqgyLSIip8n/71XIzsFnx54dl2x0b3lgu\nQSuUkvUX3eY6TJUPPGVJDNis7thMa+ifvhOqgiyJlKqta2Kqak2DRExh2CtnPzWZRxIFtg0mOsYi\nuAcgf2XoTi2emszz4fvPrXn/lu4K+X2myt/k+hPuOHzFoWFy2ViQBgxHs2XysW9cDKVn22PTB4wt\nQ0KIigiShNI/QGRsnMhCWy/pH+42Cx88q5ul6Xqk2p6/1E4vV+rXr5BnZqnGfY9Pc+LSxoUBPujB\nsjpYpOsmVPeAhg8YZmbzPNBVWWrV23M7rKmSJJH+wYQHqry2Tx4bKEkih24d45a7JpBCZq3JVJRa\n5cWBKseyEUSXqcKysBvt9d1N/wnEIhIiNpgm9z02xWcemgy+o5+6zFdaPeee/zz3/0OgygNFYg+m\nStNNvvLkDM2WhRqRyCaj1PPuWvDaXIZLkwVinv+ZAzy9UqZcbxHflsJ0HMZFmdV8k3cf3cntufWb\n2vvx8gJVpk3E+3H/OYKq9336JO//zEm+9NhUx991w+LqSi1gW7pBVdjscm7F1fr4PdiWS02++OgU\nv/9XT/Gt5+Y4O13kvsfd9y+GFtLuAexvTBFZJF/RqDZ0TnvixVrTIJeNuTSqF6tljcVCg09/e5K7\nDg7xiz94CIDLc50gIF/ROuxuCpXNKwG3KlR/148c4c9+43XBv3/se/bzH3/ydqKKFJhignty9xkO\nWRLZMRwGh401/z+7XAvG46AaYUUzAqaqoJv8zcV5lkSbwVycU8Uarx7Oko7Ia0BVQzNJdBkUTgy5\nC+VnH77Cs+dXSCciyJJAMqYEacz51XpHeX536ifMwoELsmoNnU8/dJkvPDoVfI/uTXq90A2LSl1n\nIBNjMOtu9svFJrJXeXPVY1CG+2JkEhGmFiodaeabdvQF6TDwfJJC1/z1p2c56RU6CILrLfaopx0z\nPKYqSP95v/3joTSo39qnUjcIZ0LuusntDRikALuYqqWudIL/u4z0x6lrJlZXOuHkZJ7f+MCjG/bl\n83Uzouj2p1sqNgJQpelWz3WsZbT/Xm8aJGMKAxkVURAo1XRyGZWhvviaORlmk7tF8E+dXeah5+fX\n6C39dPtAwFS5jw8mFQQBXnt0jGwy0jP998XHpvj6M7OcDg5Ya5mqpiGDKiF4upXY3r2kC/PBxvLf\nPnaCx09v7q3ng2w1ugkb7bFCb7xznJ97802AW6kaVSRyGfUlF2iEw88ObJrG7BJPB3++TkJ1/z19\nvdAzL8zy8QcudgC4DlDVJfjODSe5OlXk7z/0DNBmqtaL5EtgqhzLRJCk/83eewfIdZdX/59bp8/s\nzvaq1ap32ZIsyXLHFWxKMD0Bh5L8KCGEvCTkzQvkTUIS3hAgoYQSQguhx0AAm2bAXW6yZMnqbXuf\n3enllt8ft8yd2dnVriQL2dH5w7JWs1PuvXO/53ue85yHXx6y7vt62tM4optIoogkiUiYmLrGVKrA\n+HQOw1baHMUtky/V/P6YWu3yn/P5peBsUrXnyATf/tUxDvcn3PLf9FiWty5t4zvf2s8v9gxRPznG\nJtnq9j46kyWRLaI3+FgdC9EdDaDpBvmctiDl8aIjVc7N9H9a+Kf3ZnhiuLIW/Znv76d/LM1NW63O\nneobqkOqGmN+BsYzFbv3iek8faMp8kWdg6cTiILAvbv7yOY1t/TX1hBkaCLDz5/od1WA8UQWWRLo\nbY/y5OFx/vTTD1fsjpvq/MQ8i7lplst+t1zRTdCv0BD1uyUgB5MzedobQjTXBVjSEsGkUkGYSRdm\nLQzOwnompUoSRXwe4hUOKCyx1TRHqepuCTMxYxmCb9zayf97+056O8q7j1GbgGi6wcR0npb6gOsr\nMwyTQrrIVL7EZL6I2uAnK1rHKy9DpMEiIMvsQasOqUrliuiGQbagzSo7ypLIK6/t5cYt1rltbwgS\nj/hpawjSYZtGT4+k6BtNuZ9hssr/5njsAvYOtD7iYzpddMuJAZ+MLAkuGfLicF+CP/vXh8nkS6Rz\nJX744EmXhDXG/PS0RlwSvKKzcuSNIov0tkc5MZR0X+tv3nIFK7vqCFV9TqdsOJMu8I1fHuVQ3zQ+\nVXJJ0Td+cRSwjOqqLLrn2llsD/eXDeijiRyabrjNAQ7aG0O0xoN87zcn+Ni3nnZv1Ln8/EpVe6N1\nvqrLdY/aZGA+hU/3lHia6wKMeToloXaq/D98/Sm+++vjgFP+U+xStXUwVnTVEa3yxUGlBaBaBXPu\nAV4fSqG/j5lv/QeYpsdTZS1ErTGVj75jF0taI9SFfcykixVdxRMzOX7+eD8AJ4etYy9UGdUB8gUR\nwS9i2jPgAstWoOpF1oUL7vtyPJbzwSHdZ9o4OeW/TT31XL2pnfqID90wWdtTTyysLsocfyY4JPZM\nz+ktcTnqybHBGTTNOK9GdYcwFLPZWen9Rqa8YaqOJmhoqiyJ+c8w6zAU9ZFJF92wzkVB1zFFkWfH\nrWOmJctrmW5Y5T8AGQM0nZl0Ed0wPaSq0uNYnXU3V/nPIVW1lCrnfpmz42zqIz4M0+TQMWuzoAsi\nsq7xEqnA1a11JIoaxY4ghggv6ojTGLPu6wuNHbq4SJVu4LcXhv9pSpV3p5hIFTAME8MwSaQK7Ds+\nye1X9nDbjiUIAqSrbqjj05aKsL63geHJjPtcAZ/E2HSOIQ+xuXZzO5pucuDUlPu4XrsE9Y1fHHVj\nGcYSOeJRPw0xv3su/s8bt3LFGksNaKoLuKSq21ZbHjs4ioC1uAG0NQYZrvJMTdllpb//wx288dZV\nAIzYHSimafInn3qIf/zGnorfWWj5bz6s7YmzuruO5R0xBsYzFEo69REfdWEfTfaXRpVFl1CMT+cw\nTJMbt3YRDSp8+1fH2H9yij37RtFMk/CWZuKbm1C7LNImh1X8NulxMnEcUpXJaa6q4pi7vXjJzh5e\nf9NK/vat23nL7Wt55XXLeOmupURDKiG/zMP7R9ANkx1rrZlhXu+VaZqkcyVu29HNp95zNQArOmPo\nhsnwZJbbdnTzkf9vJ+0NIQbGZitV9+8dZmImz8BYmgf2DvH9B09yj52R1BQLEPQrdNo35VXddXz0\nHVfy+htXABZh6G2PMprIuVEGTkkwVHXjdgjNU55OQL8qsb7XCiF0yGZR01GVslLlqJSJVIFl7dZ1\nOjaV5V++t49//ObTFa8RC6tsXtEIwP6TU+7w4Hy+5A7o9ZIcpzTV3hCy/165ePbZx2u+EFKvGtFc\nH7A6+FIFt4xbTX4M02RgLE3faArTNK0yq33MfveWVbx0Vw9vvGUV4aBVwvUSnQqlqoqsjdcgVZn9\n+5H2PILPKHqUqnJwokPSnUXG68N79MAoumFy3eZ2T4l+tlKVzongkzhip5nLPb0ArBE9hvuJMyuk\nZU/Vwsp/pq6T3P0IL+v7OQCblzcSDaoL6iZeKBJ2CeyM0RBmJanSDYN/+ubTZPPaecqp8niqgFLW\nOsfea3mu8h9A75omlm5q5ffesYNrbllBQ/PcviOwlCqAbLpI32hqloJ7pveqmQJZybreKkiVbrjf\nCxETUytf345C5bz3jEfdr8BcnipHqapBqrybEb8qu17QfccnUWSR5gb7eOgaq+vC+EQBJe6n2RTp\nCFkWCLAEioXgoiJVmma4u+3zTapKmnFeTYznG45q1Bq3ZP8PfHE3n/juXp44bO3ydq5rQRQs70Z1\n99/YVI7GWID2hiC5gk6/vRis7q5nZDJbUUZ48Y4lhPwye49NuK/5kit7eN2LVtBU5+cnj/bZWTlZ\nGqJ+92a8sitGb3uUDnuBbYoFiNkX58blDfhVicHxDM31AVctam8IMTJVOSfJWUQEQaCl3rpJODdt\nh/w5HYUO3NLAGXax8+HylU382esvdwkf4H65tq5u4uZtXVy+qsnt4nOOYW97lDuvW86JoSQ/fuQU\nmu3ZKKVLiLqJHLSuVyWsoEQV/JJIxH6f4YCCT5XoH0u5C7hzzGqhvTFEXdjHttXNrFsaRxAEuprD\nbsPAFWssUuUtteUKOrphEgmorpF4ZVede/PqabW8XZ3NYU6NpHj21BS6YTAwlkY3DPYdt0jOaCLn\nelN2PztKOKDQ02YRRkcx86sy8aif6y7r4JXX9nLDlk6XkD95aJygT3a/v45S5ZQMnciKJzy5YX5F\n4k9etYnbr1zCVLKAblidkoosuotr3kOqlrZFUWSRnzzax36POdl5rWhI5WVXLeWv33wFQZ/smplF\nDNbYRtxTI+Wb/MBYBlkS2WZvFA57Ogdn0gXXKJ5I1b6ZDoynKWnlhaIlHqSkGUzM5GmN11a/Uhlr\nZz4xkyeT18gVdPemff1lHbz86l5kyergNM1KVXoymXftEd6flzTd/S6Pe278TqnPJ+KW6t3hvx5j\ns/M98C4+uw+Osrwjxk3busrl+hpG9UxOQJAEJtPWQp/yR9EEkWbKG7nx6fwZJzbkixqCAKoy/5Lk\n7bTLHjhAy+QpBNNk4/JGoiGVwfEMX77nEJ/+r2fOTmnxwCkze8l2rc47s6r8NzKZtcc3nR+lyu3+\nswmDkbOHAXtUnLGh8maluvy379QU3947RLKos+6yjjO+p7A9laH/6cP89Zd2L0hpdF9b1ymZAhmb\nVE2Plkvnlv/QOr8SZmUnn13We+DJPv7tR8+6ZKp6U1LRDGDMVqpqkapJjz/Mp0p0NlmPOdI/TUdj\niBZ76LyeyxNTZX63s5np/ZNcFrC+w40xa03zquXz4aIiVSXdIGDvUEvnmVR9+1fH+NCXHlv0FPkL\nBae7qrc9apuKs+w/McWvnhqksylEm72bDgcUKyNGN/jsD/bz5XsOcXo0RVdz2H3M4T7r5F+5vs0l\nNC/ZuYRXXNNLQ8zPxmUN7Ds+yfhMHp8q0VIf4KZtXdy2fQknh5Pcs7uPcZtUOaUNp4zmXJBepaop\nFmC9Pfag07MLam8MUdQMT8eflVHVbJtyg36ZWFh1CcwBu/Op+sZaKOkIglVuOlc4ZnYoLyaNsQCv\nfdEKOhpDzKSLfOI7e9n97Cghv0x3S5gr1jQjSyJHB2YoJYsk9o5jHkrQbitSpm6AKPBsIkOTv0xu\nRFFg8/JGnjoy4e56YzWUqvnwquuXu//f1RxGEgUeOTDCr54aIJvXSNtJ4F6zul+VWWqrOt12S/XK\nrjrSuRIf/ebTfOxbe/ngvz/GX3zuUVfxODE0w9HBGZcgbLM/M8Cm5Zb6Ew1ZryFLIi/Z2UM4oLC0\nPYokCoxN51yVCnDDMBuifursczwyleXQ6YRLvEq6YYUQxgIYpslUskAyUyQcUCoiFXIFjXxRJ25H\nPEwm867fzPqM1rUZDar4FInO5jDb1jQzYytVommywT7vxz3xIKdHU+53qzUe5Fd7BvmbrzzOVDLv\nqlRQO8NtcCLDB7/4GOlcqVz+qy+3mK/psUhctarsPFciVXDVu8a62a3pUTvtP+nZQA2OZ+hoCiNL\nAr69jzD8hc8CFsl27mqOUvXw/mF+vvsUAE0xtewFMRxSVX5ex1N41F40BicyDI5n2L62hbaGEB12\nWdt763RIVbFkk7ySk3dXREciqAjsWt/K1lVNAO5A57mQt03Ewpk8Kx6jujadQADe9dJV+GcmWHrq\nKQDu3zvEk0fGOTawsEVwLpTLf9afuYLGez/1EO/91IM89MwwD+wbsjbqVeW/03a3sGnMGpfovr/f\nPD244LXIIcdOaUsvOOWsMqnau7+//PgqpcpJ6h+dyvLDh07yF597ZN4GjJBjM7j7bq6deGqW3WDe\n96rpFHXI2aTqiadO8OwpawOkG6a7yZIwER1SZJoIttp3rG+K3c+OlhsyctXlvzmM6nn7u+6f/V2a\n8myKfKpEc33Q3Vx3NYdpabfub+mEdW+YnMqRH83S02x9LxRZ5PKVjTxxaGxBk0AuLlLlUar0BXqq\ndMM4o6pV0nQe2T/CTLo4q/vsYoFzkfe2V3YXjE5ledlVve7fYyGVocks//nzI645dWImT09rxN0d\nH+6fRhQENi1vcEcCXLWhjTuu7AEsz0Y6V+JI/zRNMb97I7tmczvb17bw3V8fZypZoCHm56qNbWxe\n3sht25cAsKG3gVdfv5yNy+J0NYe5fGUTa3vi7sLb5anftzVY78fxpAzaC1Wn5zFruut59nQC0zTZ\nbxvhFanysswXF3jDXQCc+jjMLsWt6qon5JfZd3ySPUcnWN/bgCSKqIrEik4rvLOjMURxIs8rr1nG\nSjv8Lly0rtWcbtBUVfbauqqZdK7E7oOj1muG5laqamFpW5T3vGoj7331JkRRwKdKHB2Y4Ws/O8In\nv7fPLdl4SRXA9jUttDUEXaP5NZva+ad37qIh6ufg6QRre+ppiQdZ3xunIerj0QOjmCbcfmUPAtb1\n4j7X2hb+8ve2sN1Wyrzwq7JblmuMeUiVfRwiIZVlHTGODcxw7+4+JEnktTdYRNFRjp33ODCWZmw6\nR2dTCFkSkUSBQlF3F7f6iI/2hhABn8Qf3LHWLQfHIz5UWaxox9/Q21A2+JoGLfUB2hqCnBicwbAj\nKvpGUy4h27isgeHJLCeHUzxyYMT9Pi5pidRcgLwNGM7Ov8VDjq6/rANgVoq7c//RDdPNpGqqQaqc\n8+l0JWq6wcnhJCs6Y4QCCp2P30tq96NAubQqiYJLqu57apC87SVrjni8j65SVV4cGmJ+WuJBnrXz\n2JwmgsvsUupae0LASbsL1jBNMvkSAZ9EsWh9V7O69VqTM3l0QcQvYZWyr10G1M7c8sL6ji8gh84p\nF9mkCmB9e4jBj3+U1id+xptu6OHv/mAHAPvtz5EraBXhxAtFwjWqFzFNk/6xtDs54Ys/PsiXfnKI\nnzx6Gjzd2qau0+eMhTFNpFn3Mo0v33OIr9x7mB8tcKqEc64cT5VhB4B6S2OqWTtSAcpeoF88OcD3\nHzjJaCJXkYdWjbA9lSEvh2jPTywuSNjQKegmXa0xcqJKYXqG7/3G8g96y3+KYCLaRKqtvnzfyKRz\nbie79RnnVqpMz7rvKK+CPPsaqij/2VUE59rubA7T2WUR/58/cJgj/dP0j6WRRKGiqrFzXSvZgjZv\n04qDi4ZUZfMlsnnNVT8WqlR95D/38KeffojDfQm3lOFFOlfivx8+7UqlC+2AutCYThUJ+GSXGAHs\nWNfCW25fwxZ7twdw5YZWhiYy/PrpIZe0gKUk1Ud9qIpIOleiPqJ6h2AIAAAgAElEQVQiSyLre+Mo\nskhjXfnCbbNfo38sXUEyREHgdS9a4f69IeonHvXz7js3uv4gWRK5dXs3iiwR8Mm863c20BDzs2l5\nI0taI64iAGVvlRNJ4BilvWrWmp56kpki+09OuSGbVr6NzvGhGQbH01Zo2zmU/qpxzSaLMHhjFgCW\nd8b45HuucX1jXlVrra08XL2pnY+9axc717WyuTWGALx8fQdb7VbbmFJJbjb0xpElkScPWzf1xSpV\n1vtoZH2v9V6cLLdNyxo4NjjjlibCVQGHL9rSyYfftqOiW6U+4uOu21azvjfOO16+nj99zWbe++rN\ndDSFKWoGsZDKHbt6+Ni7drG0rZLcL+uIzUlq19kqpVdJdEtyQZVl7TEmZvI89MwwV21oZYWtMjnN\nKM61ucf2W3XZO0S/KvGTR0/ziW8/7b7/19+4gg+8aRvxqJ9/fPuVfPht27n2sg7uvG5Zxftb0Rlz\n59yJmIQDCr3tUfYen+Tdn3iAE8NJMnnNVWCvWNNCOKDQGPOz+9kxppJ5BKwcsUSNjdhJTzOJs1DE\no35WdtXxBy9dS8RWmn7w4EkGx9Pc8+hpjvRPV+yaD9nXu5eMOnB+31nQTo+mKGkGyztihKuGAztE\nakVnjLHpHGPTOU4MJZHsRaspVr7OnYWoukS0tqeew31Wxtf+k5O0N4Zc5bHbPh+D9ozMbF7DNK0N\nSrFgnXMD6/49lbRIlc9u4GiqC6DI4hl9VfmitqAcOm/5T5u2lCgjl3M/z84uP63xIK3xoBs+/PWf\nH+FT//WM29G8EBiGyXS6iCKLFDWDfFF3A2b//g92cLu9QT0+ODPLqN5nK1WYVEQVABWL8kKJXrWn\nyqyhVBXyBQwEdERK+Uqbi9Op7A3G9eYDVitmqk9ClgUKcgjZ1BdFqgxNJ6eZLG2LIIbC+PWCu0m2\njOp2+U8wkU2LIPU0l9exTNVMvVlTCeYo/81FqrJ2id2BsxHbvqYFVRZZ3V3Psu56DEUlYJb49Z5B\n+sfStDWEKu5na3rqCfgktwN+PlwUpErTDZ44PI5umGyzd8MLVaqODcyQypb4yH/u4RPf2Tfr3//r\n/hP86OFTxO068cVKqhLpAvURX8VC/6ZbVnNlVRr4znWtNET9REMq/+u1l7k39CWtEURBcGelXb3R\nCp6887plvPuVG91aNkBrQ5mBe8kWWL4Up/zmHLOFIBxQ+NBd2yoW45BfYXlnjCcOj9E/lubEcJJI\nUKnoGlzXYy3IX/rJQQzTdG9WM+kin7l7P1/76WF7ntz5GzPxxltX80/v3DXnzvjOa5exa0Mrm231\nDSxPVjigsK6n3vVFNfpV3rexh7X1YV66pIkb2uNsa6okI6pi1fB1w1rYZencvnJ//YdX8s5XbGDr\n6mZ0w3TT7sPB+Tt6HKxbGue9r95cEXngeNs2LmtAFIR5fV+14C3FOXDKf9GQynJb5TNMk1u2d88i\nEfGIH0kU3EXGiZlwbsCOJyIe8REL+9yNRzig0NYQYnlHjBu3dlU8ZySoElTsMqxpEAmqbF5ubU6y\nBY1v3Wcl0Dvl0d72KP/87qu4aVsXA+NpDp1OEA2rNMT8ZAsaTx4erzDsHvPkhYmecu/733A5O9a2\nugrx+HSeT3xnL9/59XE+c/czFarXob4E4YDiqvNeRO3zmcwWeerIOP/wH1Zpa3lnjIhaOVR2eDKL\nX5VY3V3PWCLHx775NAKWGgBWfIKLGkoVWN/DQknnBw+e5Ej/jFvOB/DZx3EsYb13Z0HubglTKNnK\ng8+0hoAn8yBKCA4REAUaY/4zmnwdNfqMsImikc+5GUhGPucSDodorequ49mTk+w5Ou5OJdh/8swq\ng4MZ2/vmlEan0wUGxjMEfDLN9QF+55peXrxjCadGUpQ83XamVuK0rVQJzI5UePzgGLGwynWXdTBm\nj445I6o8VYId/un1VBWzBXRBQhMk8tnKY+3YL0zT8uw21fndc3hyOMk7Pn5/mQhiB4AGJfJy0CJV\nixg2ns8VKJkCa3viROsjNAdFd7anZme6gfWdFLHKfvFg+foXqkbVzOepqvn/UuU1VD0pxFGqOpvD\n/OufXktXcxhREFAjYTrDIk8dGedQX8K9LziQRJFlHTGOzjN43MFFQare9ne/4Mv3HKK5PuCWWRai\nVNU0DVb9bHgiw9K2CH//BzuIR3389LF+d6THxYREqkBdWHVJVUPUV7PbTZZE3vuaTbzvdZdRH/Gx\ntD1Kc33AXcQcL82NdvxCYyzgKgkOosHyjbwpNrv08KZbrfyXjsa5I/8XiitWNzMwnuFD//4Yu58d\nnfWc8aify1Y0Mp0usnVVs1tKOtQ3TSJV4NRIikxeOyeTejVEQZilUnnRWBfgLS9ZW7HYtTWE+Jc/\nvto16juo8ykIgoAsitzY0UCdbza5cUpMtTr/Fot1vQ1sWdXk+ueO2L6RyBnapOeD4wXyksjFYEVX\nHa++fjmv9aicQb+MKos0RH0saYmgKiJbVjXTUh90jdg32UTIWXizBQ1ZEl0yX91YUjfPOauFkE0G\nIgGJSFBhy6om/u3Pr6e7JcyxgRla6gMVGWWCYC0GYE0DqA/73AHVn777GXY/a5Vws/mS2zzgvP9q\niKLAm1+8husv73BJoYl1k3fUynyxbFKf9d7t85nMFPn6z4+gGyYt8SB1YR+NRnlj+K2fHuBI/zTL\nO2LcfEUXN1zeQSig8PaXr6fe9sA1eK470/FU6ZWL1+bljWxf28KPHzmNphtuOR/K91QntsHxQK7q\nqqdkK1WKavLlew5Zfk5Jqnj+hqi/QqGrhYWW/xzVpjRZJkhGLueWxpyS4I61LeQKGp/83jO0NVge\nGq/KUCjp/OyxvjkDWh2Ssarb2jAkM0UGxtN0NoVcRdTpsp3wpLv/4rHT5AoazXV+BARMz6VhmiaH\n+6fZ0NtAazxIrqDNyhwcmcpWEBzweKp8fhAlVNPpjCt3yJlaCVOW0QWRQr5M3Eua4TYxAHQ0heho\nDLsetx89fIpCUZ8VcBrySxTkELros5oriiUGPv5Rcsdrr51HDoySmMySyRUxBZG1PXHEQACfUXRL\n2JlcqXxPtc+jZBrE/OV7u2RWkv3q7r+5yn/Y43Gq1fQJm1Q12yV275rqfawYDNEcsGIqiiXD3dh5\nsbKzjsGJDKNVzVfVeA6nTC4cTjr1TVu7XCabL2pWzP88nQq1uvlS2VJFGOLYdI7V3fVWuUqVmUpm\nuHd3Hy+/ainqeVyozxbZfAmfKjGVyrO+IY5flQn6ZHfRrAXvv91162qKnl3nu1+5kUy+VKFEVEMQ\nBMtfMpScpVSBpYbdfs1yJifPXdXburqZb//qOHVhlYmZfE0V5I9euZGx6RzRoOKaKp3AwKJmcHxw\nxiUmz0cssXc9i1WA5oNT+j02MIMsCQse4VMLW1c1MZnMuyXGxUIUBG7d3l3xM1kS+T9v3EpjnR9F\nFvnL39tKg0f5/Od3X13x+Fdc08tnf3CAeMQ3Z5lxsSpffUhBn4A33bzS/V1RENi5rpW+0WO87saV\ns56zNR5AlkQ03bAiNzxE7sDJKa5c3+Z2qcqSgKbPfY+6amMbO9a1cGIoyVgiSypbYmgiS1s86Lbp\nO6XOWp815Jc5eDpBIlXgZVct5eqNlmrdpJcX3Qef7CMrB9ixrgW/KvO7N69y/+1Re7GKe5Qq11NV\n5bsRRYG33b6Wqze2EQ2pFb5HsIOEE3lM0+T0aMpWCYOUCtZnDwWh/6St0EhSxeIXj/oqjP+1kMmX\nKjwsc8JejLXJMgkwch6lKmGRqlXd9fz+7ev47n1HecfL1/Pw/hF+9ng/p0dSPLhvmOZ4gG/ed4x7\nH+vjb9+6w430ME2TI/3T7DsxiSqLbF3VzI8fOc2X7jnEWCLHdbZXDizVUACGxtJ02j979JkhUOKs\n7a4nMz1a4Q+aThdJ50osaYm4uWFjiZxb6h2ZyvK/P/8oiizyuf91nft7zrEUJAnBp6Iadv5aoTxt\nQDJ1RFlGMw2KHlI1mbSaGEJ+mUxeo7MpTFHTeebEJAPjaXfY+eG+adhVPsxBv8CIWs+BzttoGs3y\nhY8/xNrhDMFjRwksKzfPgOWVuu9HB1m1oRV/rkggECLol0kEAih6gnTOGlc0mcyzY11rxWf68Ju3\ncvJ0uQzqJVU+Vaogvb9+epDUU324V7i3/GeHjlaj3yaoK7qs0vhcxF0KBpH0Iu977Wb2n5py36cX\njuDzF59/lGs2tfO+N26r+VwXBakC+MS7r3I7XgQB7nm0D00zed2NK+b8Hae1/NXXL3el68lk3iVV\nTquxsxPfsa6F7/3mBGBdbPMRlwsB3TD431/YTVdzmJl00TWp37q9e2E3GJj1uGhInZWwXQutcYtU\n1VKqoPbu+2xQF/bxD3+4g2hI5eH9I25rezWcnYTz3g+eThD0WaNO8kV9wcfjYoSrVC3gvCwUAZ9M\nfcRHIlUgElTPycQfC/t49fXLz/zARcLrnau18/PiijUthAJKhV/omk1tTCULbgPDYiELJjoQrkrp\nftGWTlZ21c3yjYEl83c0hjg9miIe8bO8I8otV3TRN5p2GyqcgNhl7TG7KWSe9yCJfOiubew9NsE/\nf3cfA+Npdq5r5c0vXkM6V+Laze1z/m4kqHJ0YAZBsIzvzndjbaiEs5wotkG5Vgm2vd5PHoj4PMTR\nVapmdzGJYlmpq4C9K8/kLX9N32iKJS1h/D4ZwTQx8zpbVtfRuGEtn//hs5iSVBHZEI/6SWaKlDRj\nzg7eTF5z1fb54CpVEx5SlS9nczlKFcArrlvOrrXNCILA9rUt/PSxfv72q0+gG6ZbXp1OF3n80ChX\nb2xHFAWODszwkf+0cvI2LmugqyXMK65e6vr9lraVSXDIr3DZyiaO7znskipT15F8Ass7ouzdN0rJ\nQ6r67Sy3ruaw24gwlsixrMNarL9jV1BmzUu0FZnTExnyyKhGZTDm+HQO2TSQfCqaVkL0eKqcJqGV\nXXXsOTpBR2OIQsmKYfnKvYeQZZEtq5p48vA4JXsDu7KrjqBPwBAtelAA6nwSI5FlrPOQ8V/tGWRt\nTz1+BEwTJkbTtJU0gnFrIyL6/ciaFSrbN5bCNK0pDKZZjlOoD8pM+cvXhGwaqIpIqWTQGPO73X8P\n7B3iq/ceZnVqglVY3qnq8l8tUnV8KElbQ9C1OMyVdSiGQpTGxljTE2dNre8AVtNQLKxiGCb37x3i\nfTUfdZGQqiWtEZdQQbl199FnR3jNDcvnXOAdUrWhN45umBapmsm7N0un1dhZsF+8Ywk9bVH+6ZtP\nM5Us/NZJ1cCYlX5+4OQUkiiw1R6z4fiKnkssaYnwxKGxmkrV+YZjeL1m09wLiAOnbR+snf7P7FTn\nOy7AMXmu0NlstcE31DAknwscVfemKj/R8xXrqm5md922BiiH4S4aem0CIUtiTULloLPZIlX1UR+K\nLPGaG1Zw/94hvnzPIYYmMowlcgiCdd9yOm3PBK/SeuWG1lmftRac9u3NdgaTg1AhiWOT39AV4bFJ\nkZ7W2Z9HEU3yVJVMtNpK1fww3f/2jaYYmshw09Yua4abaWLmdMRIiR1rWxEFgbpv/6bCs+WUUBOp\nPM31wdnPbppk86VZKfy134r1XkpTnvJfNucOGdYSlTEKzmajuyXCi3cu4UcPn0ISBZLZEpevbKJ/\nLMWPHznNN+87xu9c3VuhjKxbGkcUBO7YtZTbr+xhaDLrNvk4uHlbF798rPxZRdOgrSFENDC74arf\n0/2syCIC5fw2oCKkuVDUXQLgnL8H94+xUhNRRUepKs/WlEwdxadiZA1KBetYHB2Y5rM/OEDQJ3P1\npnYOnJqitz2KJIlEQyrHB5Ncf1kH63vjPHpglPueGuBb9x3jDTetJG5fbrKe5xlJ5U09MQ7m2igU\nLE/RxEyOr/30MD2tEX7f7vCcmsjQbpqotgVCDASQSpZq5ng/m+oClREUWomIT8LRMRUMulsiDE9k\niAQUt8Tp+C1FWzUVFKWi/Dczk6OgW/5sR302TctzunlFo0ti57KRSMEQhez8HaqqIvHRd1yJacLX\nfnp4zsddFKRq04qmmj9PZUscs5lzSdP5xHf2YRgmb7ptNa3xoNsqaqV+29PLPcY0p9W4yVaqrMDJ\nwKzHzYeDp6b4zPf388G7ttVsfV4sBsfTNNcHUGSJI54wsXVL464MfCFw/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pojg0meOjzG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\nB3Hd4J3I1Sfx1MMnMDt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PqDGuftN6XPeWjVizsb3l+wsFYz2lbHCMDUxKI3AnJgOiCcqs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mhpwkZayEJiwzBLQrtFnuvQFi7rDJqLkQBVw5kqILcfmqaz4otRBxOGjrquIQpbCphX7iDx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\nQ0NDQ0NDQ6MB+P/0aFoAOhitPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125ca470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_random_series(df, n_series):\n",
    "    \n",
    "    sample = df.sample(n_series, random_state=8)\n",
    "    page_labels = sample['Page'].tolist()\n",
    "    series_samples = sample.loc[:,data_start_date:data_end_date]\n",
    "    \n",
    "    plt.figure(figsize=(10,6))\n",
    "    \n",
    "    for i in range(series_samples.shape[0]):\n",
    "        np.log1p(pd.Series(series_samples.iloc[i]).astype(np.float64)).plot(linewidth=1.5)\n",
    "    \n",
    "    plt.title('Randomly Selected Wikipedia Page Daily Views Over Time (Log(views) + 1)')\n",
    "    plt.legend(page_labels)\n",
    "    \n",
    "plot_random_series(df, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Formatting the Data for Modeling \n",
    "\n",
    "Sadly we can't just throw the dataframe we've created into keras and let it work its magic. Instead, we have to set up a few data transformation steps to extract nice numpy arrays that we can pass to keras. But even before doing that, we have to know how to appropriately partition the time series into encoding and decoding (prediction) intervals for the purposes of training and validation.\n",
    "\n",
    "We'll use a style of **walk-forward validation**, where our validation set spans the same time-range as our training set, but shifted forward in time (in this case by 14 days). This way, we simulate how our model will perform on unseen data that comes in the future. \n",
    "\n",
    "[Artur Suilin](https://github.com/Arturus/kaggle-web-traffic/blob/master/how_it_works.md) has created a very nice image that visualizes this validation style and contrasts it with traditional validation. I highly recommend checking out his entire repo, as he's implemented a truly state of the art (and competition winning) seq2seq model on this data set. \n",
    "\n",
    "![architecture](images/ArturSuilin_validation.png)\n",
    "\n",
    "### Train and Validation Series Partioning\n",
    "\n",
    "We need to create 4 sub-segments of the data:\n",
    "\n",
    "    1. Train encoding period\n",
    "    2. Train decoding period (train targets, 14 days)\n",
    "    3. Validation encoding period\n",
    "    4. Validation decoding period (validation targets, 14 days)\n",
    "    \n",
    "We'll do this by finding the appropriate start and end dates for each segment. Starting from the end of the data we've loaded, we'll work backwards to get validation and training prediction intervals. Then we'll work forward from the start to get training and validation encoding intervals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "pred_steps = 14\n",
    "pred_length=timedelta(pred_steps)\n",
    "\n",
    "first_day = pd.to_datetime(data_start_date) \n",
    "last_day = pd.to_datetime(data_end_date)\n",
    "\n",
    "val_pred_start = last_day - pred_length + timedelta(1)\n",
    "val_pred_end = last_day\n",
    "\n",
    "train_pred_start = val_pred_start - pred_length\n",
    "train_pred_end = val_pred_start - timedelta(days=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "enc_length = train_pred_start - first_day\n",
    "\n",
    "train_enc_start = first_day\n",
    "train_enc_end = train_enc_start + enc_length - timedelta(1)\n",
    "\n",
    "val_enc_start = train_enc_start + pred_length\n",
    "val_enc_end = val_enc_start + enc_length - timedelta(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train encoding: 2015-07-01 00:00:00 - 2016-12-03 00:00:00\n",
      "Train prediction: 2016-12-04 00:00:00 - 2016-12-17 00:00:00 \n",
      "\n",
      "Val encoding: 2015-07-15 00:00:00 - 2016-12-17 00:00:00\n",
      "Val prediction: 2016-12-18 00:00:00 - 2016-12-31 00:00:00\n",
      "\n",
      "Encoding interval: 522\n",
      "Prediction interval: 14\n"
     ]
    }
   ],
   "source": [
    "print('Train encoding:', train_enc_start, '-', train_enc_end)\n",
    "print('Train prediction:', train_pred_start, '-', train_pred_end, '\\n')\n",
    "print('Val encoding:', val_enc_start, '-', val_enc_end)\n",
    "print('Val prediction:', val_pred_start, '-', val_pred_end)\n",
    "\n",
    "print('\\nEncoding interval:', enc_length.days)\n",
    "print('Prediction interval:', pred_length.days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Data Formatting\n",
    "\n",
    "Now that we have the time segment dates, we'll define the functions we need to extract the data in keras friendly format. Here are the steps:\n",
    "\n",
    "* Pull the time series into an array, save a date_to_index mapping as a utility for referencing into the array \n",
    "* Create function to extract specified time interval from all the series \n",
    "* Create functions to transform all the series. \n",
    "    - Here we smooth out the scale by taking log1p and de-meaning each series using the encoder series mean, then reshape to the **(n_series, n_timesteps, n_features) tensor format** that keras will expect. \n",
    "    - Note that if we want to generate true predictions instead of log scale ones, we can easily apply a reverse transformation at prediction time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "date_to_index = pd.Series(index=pd.Index([pd.to_datetime(c) for c in df.columns[1:]]),\n",
    "                          data=[i for i in range(len(df.columns[1:]))])\n",
    "\n",
    "series_array = df[df.columns[1:]].values\n",
    "\n",
    "def get_time_block_series(series_array, date_to_index, start_date, end_date):\n",
    "    \n",
    "    inds = date_to_index[start_date:end_date]\n",
    "    return series_array[:,inds]\n",
    "\n",
    "def transform_series_encode(series_array):\n",
    "    \n",
    "    series_array = np.log1p(np.nan_to_num(series_array)) # filling NaN with 0\n",
    "    series_mean = series_array.mean(axis=1).reshape(-1,1) \n",
    "    series_array = series_array - series_mean\n",
    "    series_array = series_array.reshape((series_array.shape[0],series_array.shape[1], 1))\n",
    "    \n",
    "    return series_array, series_mean\n",
    "\n",
    "def transform_series_decode(series_array, encode_series_mean):\n",
    "    \n",
    "    series_array = np.log1p(np.nan_to_num(series_array)) # filling NaN with 0\n",
    "    series_array = series_array - encode_series_mean\n",
    "    series_array = series_array.reshape((series_array.shape[0],series_array.shape[1], 1))\n",
    "    \n",
    "    return series_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Building the Model - Training Architecture\n",
    "\n",
    "This architecture / code is adapted from the excellent [keras blog introduction to seq2seq](https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html). Chollet's piece shows the more classic seq2seq application to machine translation, but the steps we need to take here are very similar.\n",
    "\n",
    "Note that we'll use **teacher forcing**, where during training, the true series values (lagged by one time step) are fed as inputs to the decoder. Intuitively, we are trying to teach the NN how to condition on previous time steps to predict the next. At prediction time, the true values in this process will be replaced by predicted values for each previous time step.\n",
    "\n",
    "This image created by [Artur Suilin](https://github.com/Arturus/kaggle-web-traffic/blob/master/how_it_works.md) captures the architecture we use well (we use LSTM instead of GRU).\n",
    "\n",
    "![architecture](images/ArturSuilin_encoder-decoder.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Input, LSTM, Dense\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "latent_dim = 50 # LSTM hidden units\n",
    "dropout = .20 \n",
    "\n",
    "# Define an input series and encode it with an LSTM. \n",
    "encoder_inputs = Input(shape=(None, 1)) \n",
    "encoder = LSTM(latent_dim, dropout=dropout, return_state=True)\n",
    "encoder_outputs, state_h, state_c = encoder(encoder_inputs)\n",
    "\n",
    "# We discard `encoder_outputs` and only keep the final states. These represent the \"context\"\n",
    "# vector that we use as the basis for decoding.\n",
    "encoder_states = [state_h, state_c]\n",
    "\n",
    "# Set up the decoder, using `encoder_states` as initial state.\n",
    "# This is where teacher forcing inputs are fed in.\n",
    "decoder_inputs = Input(shape=(None, 1)) \n",
    "\n",
    "# We set up our decoder using `encoder_states` as initial state.  \n",
    "# We return full output sequences and return internal states as well. \n",
    "# We don't use the return states in the training model, but we will use them in inference.\n",
    "decoder_lstm = LSTM(latent_dim, dropout=dropout, return_sequences=True, return_state=True)\n",
    "decoder_outputs, _, _ = decoder_lstm(decoder_inputs,\n",
    "                                     initial_state=encoder_states)\n",
    "\n",
    "decoder_dense = Dense(1) # 1 continuous output at each timestep\n",
    "decoder_outputs = decoder_dense(decoder_outputs)\n",
    "\n",
    "# Define the model that will turn\n",
    "# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`\n",
    "model = Model([encoder_inputs, decoder_inputs], decoder_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, 1)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, None, 1)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   [(None, 50), (None,  10400       input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lstm_2 (LSTM)                   [(None, None, 50), ( 10400       input_2[0][0]                    \n",
      "                                                                 lstm_1[0][1]                     \n",
      "                                                                 lstm_1[0][2]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, None, 1)      51          lstm_2[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 20,851\n",
      "Trainable params: 20,851\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our training architecture defined, we're ready to train the model! This will take some time if you're not running fancy hardware (read GPU). We'll leverage the transformer utility functions we defined earlier, and train using mean absolute error loss. \n",
    "\n",
    "For better results, you could try using more data, adjusting the hyperparameters, tuning the learning rate and number of epochs, etc.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16000 samples, validate on 4000 samples\n",
      "Epoch 1/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.7061 - val_loss: 0.5494\n",
      "Epoch 2/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.5738 - val_loss: 0.4142\n",
      "Epoch 3/100\n",
      "16000/16000 [==============================] - 29s 2ms/step - loss: 0.4937 - val_loss: 0.3723\n",
      "Epoch 4/100\n",
      "16000/16000 [==============================] - 29s 2ms/step - loss: 0.4591 - val_loss: 0.3542\n",
      "Epoch 5/100\n",
      "16000/16000 [==============================] - 30s 2ms/step - loss: 0.4445 - val_loss: 0.3331\n",
      "Epoch 6/100\n",
      "16000/16000 [==============================] - 30s 2ms/step - loss: 0.4276 - val_loss: 0.3180\n",
      "Epoch 7/100\n",
      "16000/16000 [==============================] - 29s 2ms/step - loss: 0.4156 - val_loss: 0.3148\n",
      "Epoch 8/100\n",
      "16000/16000 [==============================] - 29s 2ms/step - loss: 0.4068 - val_loss: 0.3051\n",
      "Epoch 9/100\n",
      "16000/16000 [==============================] - 29s 2ms/step - loss: 0.3978 - val_loss: 0.3058\n",
      "Epoch 10/100\n",
      "16000/16000 [==============================] - 32s 2ms/step - loss: 0.3923 - val_loss: 0.3003\n",
      "Epoch 11/100\n",
      "16000/16000 [==============================] - 30s 2ms/step - loss: 0.3865 - val_loss: 0.3019\n",
      "Epoch 12/100\n",
      "16000/16000 [==============================] - 30s 2ms/step - loss: 0.3781 - val_loss: 0.3026\n",
      "Epoch 13/100\n",
      "16000/16000 [==============================] - 30s 2ms/step - loss: 0.3713 - val_loss: 0.3024\n",
      "Epoch 14/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3648 - val_loss: 0.3004\n",
      "Epoch 15/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3617 - val_loss: 0.2994\n",
      "Epoch 16/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3551 - val_loss: 0.2974\n",
      "Epoch 17/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3532 - val_loss: 0.2916\n",
      "Epoch 18/100\n",
      "16000/16000 [==============================] - 37s 2ms/step - loss: 0.3494 - val_loss: 0.2955\n",
      "Epoch 19/100\n",
      "16000/16000 [==============================] - 37s 2ms/step - loss: 0.3470 - val_loss: 0.2887\n",
      "Epoch 20/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3435 - val_loss: 0.2893\n",
      "Epoch 21/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3412 - val_loss: 0.2909\n",
      "Epoch 22/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3403 - val_loss: 0.2934\n",
      "Epoch 23/100\n",
      "16000/16000 [==============================] - 37s 2ms/step - loss: 0.3381 - val_loss: 0.2922\n",
      "Epoch 24/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3349 - val_loss: 0.2948\n",
      "Epoch 25/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3331 - val_loss: 0.2902\n",
      "Epoch 26/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3324 - val_loss: 0.2914\n",
      "Epoch 27/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3299 - val_loss: 0.2893\n",
      "Epoch 28/100\n",
      "16000/16000 [==============================] - 32s 2ms/step - loss: 0.3269 - val_loss: 0.2890\n",
      "Epoch 29/100\n",
      "16000/16000 [==============================] - 32s 2ms/step - loss: 0.3268 - val_loss: 0.2896\n",
      "Epoch 30/100\n",
      "16000/16000 [==============================] - 32s 2ms/step - loss: 0.3244 - val_loss: 0.2866\n",
      "Epoch 31/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3227 - val_loss: 0.2876\n",
      "Epoch 32/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3244 - val_loss: 0.2941\n",
      "Epoch 33/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3227 - val_loss: 0.2896\n",
      "Epoch 34/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3217 - val_loss: 0.2851\n",
      "Epoch 35/100\n",
      "16000/16000 [==============================] - 32s 2ms/step - loss: 0.3208 - val_loss: 0.2845\n",
      "Epoch 36/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3187 - val_loss: 0.2851\n",
      "Epoch 37/100\n",
      "16000/16000 [==============================] - 39s 2ms/step - loss: 0.3176 - val_loss: 0.2874\n",
      "Epoch 38/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3175 - val_loss: 0.2864\n",
      "Epoch 39/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3174 - val_loss: 0.2875\n",
      "Epoch 40/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3174 - val_loss: 0.2830\n",
      "Epoch 41/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3161 - val_loss: 0.2835\n",
      "Epoch 42/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3151 - val_loss: 0.2809\n",
      "Epoch 43/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3148 - val_loss: 0.2808\n",
      "Epoch 44/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3137 - val_loss: 0.2829\n",
      "Epoch 45/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3139 - val_loss: 0.2876\n",
      "Epoch 46/100\n",
      "16000/16000 [==============================] - 32s 2ms/step - loss: 0.3138 - val_loss: 0.2823\n",
      "Epoch 47/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3131 - val_loss: 0.2800\n",
      "Epoch 48/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3133 - val_loss: 0.2805\n",
      "Epoch 49/100\n",
      "16000/16000 [==============================] - 31s 2ms/step - loss: 0.3126 - val_loss: 0.2797\n",
      "Epoch 50/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3125 - val_loss: 0.2870\n",
      "Epoch 51/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3126 - val_loss: 0.2803\n",
      "Epoch 52/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3117 - val_loss: 0.2825\n",
      "Epoch 53/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3124 - val_loss: 0.2817\n",
      "Epoch 54/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3106 - val_loss: 0.2788\n",
      "Epoch 55/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3101 - val_loss: 0.2796\n",
      "Epoch 56/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3100 - val_loss: 0.2808\n",
      "Epoch 57/100\n",
      "16000/16000 [==============================] - 37s 2ms/step - loss: 0.3096 - val_loss: 0.2802\n",
      "Epoch 58/100\n",
      "16000/16000 [==============================] - 37s 2ms/step - loss: 0.3096 - val_loss: 0.2776\n",
      "Epoch 59/100\n",
      "16000/16000 [==============================] - 38s 2ms/step - loss: 0.3093 - val_loss: 0.2784\n",
      "Epoch 60/100\n",
      "16000/16000 [==============================] - 44s 3ms/step - loss: 0.3098 - val_loss: 0.2862\n",
      "Epoch 61/100\n",
      "16000/16000 [==============================] - 37s 2ms/step - loss: 0.3093 - val_loss: 0.2770\n",
      "Epoch 62/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3095 - val_loss: 0.2773\n",
      "Epoch 63/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3085 - val_loss: 0.2777\n",
      "Epoch 64/100\n",
      "16000/16000 [==============================] - 38s 2ms/step - loss: 0.3082 - val_loss: 0.2829\n",
      "Epoch 65/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3083 - val_loss: 0.2774\n",
      "Epoch 66/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3090 - val_loss: 0.2851\n",
      "Epoch 67/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3076 - val_loss: 0.2816\n",
      "Epoch 68/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3077 - val_loss: 0.2851\n",
      "Epoch 69/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3071 - val_loss: 0.2792\n",
      "Epoch 70/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3070 - val_loss: 0.2775\n",
      "Epoch 71/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3072 - val_loss: 0.2880\n",
      "Epoch 72/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3071 - val_loss: 0.2809\n",
      "Epoch 73/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3075 - val_loss: 0.2823\n",
      "Epoch 74/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3060 - val_loss: 0.2860\n",
      "Epoch 75/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3058 - val_loss: 0.2833\n",
      "Epoch 76/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3062 - val_loss: 0.2837\n",
      "Epoch 77/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3059 - val_loss: 0.2787\n",
      "Epoch 78/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3056 - val_loss: 0.2786\n",
      "Epoch 79/100\n",
      "16000/16000 [==============================] - 37s 2ms/step - loss: 0.3194 - val_loss: 0.2874\n",
      "Epoch 80/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3152 - val_loss: 0.2819\n",
      "Epoch 81/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3133 - val_loss: 0.2804\n",
      "Epoch 82/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3102 - val_loss: 0.2765\n",
      "Epoch 83/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3098 - val_loss: 0.2787\n",
      "Epoch 84/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3104 - val_loss: 0.2769\n",
      "Epoch 85/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3100 - val_loss: 0.2781\n",
      "Epoch 86/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3096 - val_loss: 0.2807\n",
      "Epoch 87/100\n",
      "16000/16000 [==============================] - 32s 2ms/step - loss: 0.3081 - val_loss: 0.2789\n",
      "Epoch 88/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3081 - val_loss: 0.2779\n",
      "Epoch 89/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3077 - val_loss: 0.2795\n",
      "Epoch 90/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3066 - val_loss: 0.2775\n",
      "Epoch 91/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3069 - val_loss: 0.2771\n",
      "Epoch 92/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3069 - val_loss: 0.2790\n",
      "Epoch 93/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3067 - val_loss: 0.2802\n",
      "Epoch 94/100\n",
      "16000/16000 [==============================] - 35s 2ms/step - loss: 0.3054 - val_loss: 0.2785\n",
      "Epoch 95/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3063 - val_loss: 0.2786\n",
      "Epoch 96/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3065 - val_loss: 0.2809\n",
      "Epoch 97/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3062 - val_loss: 0.2808\n",
      "Epoch 98/100\n",
      "16000/16000 [==============================] - 33s 2ms/step - loss: 0.3047 - val_loss: 0.2806\n",
      "Epoch 99/100\n",
      "16000/16000 [==============================] - 34s 2ms/step - loss: 0.3053 - val_loss: 0.2775\n",
      "Epoch 100/100\n",
      "16000/16000 [==============================] - 36s 2ms/step - loss: 0.3042 - val_loss: 0.2761\n"
     ]
    }
   ],
   "source": [
    "first_n_samples = 20000\n",
    "batch_size = 2**11\n",
    "epochs = 100\n",
    "\n",
    "# sample of series from train_enc_start to train_enc_end  \n",
    "encoder_input_data = get_time_block_series(series_array, date_to_index, \n",
    "                                           train_enc_start, train_enc_end)[:first_n_samples]\n",
    "encoder_input_data, encode_series_mean = transform_series_encode(encoder_input_data)\n",
    "\n",
    "# sample of series from train_pred_start to train_pred_end \n",
    "decoder_target_data = get_time_block_series(series_array, date_to_index, \n",
    "                                            train_pred_start, train_pred_end)[:first_n_samples]\n",
    "decoder_target_data = transform_series_decode(decoder_target_data, encode_series_mean)\n",
    "\n",
    "# lagged target series for teacher forcing\n",
    "decoder_input_data = np.zeros(decoder_target_data.shape)\n",
    "decoder_input_data[:,1:,0] = decoder_target_data[:,:-1,0]\n",
    "decoder_input_data[:,0,0] = encoder_input_data[:,-1,0]\n",
    "\n",
    "model.compile(Adam(), loss='mean_absolute_error')\n",
    "history = model.fit([encoder_input_data, decoder_input_data], decoder_target_data,\n",
    "                     batch_size=batch_size,\n",
    "                     epochs=epochs,\n",
    "                     validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's typically a good idea to look at the convergence curve of train/validation loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x108bad320>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s80+T7F8X2Odp8THXjE3DaNDx4ap91Hl8gQ8QQohOotkEMHv2bL755ht+/vOf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\niUFn4K5BPyfRHo+qqfwz8y22FG3HoOj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122d7c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Mean Absolute Error Loss')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Building the Model - Inference Architecture\n",
    "\n",
    "Not done with architecture yet! We need to use keras to define an inference model that draws on our neural network to actually generate predictions. In a nutshell, this architecture starts by encoding the input series, then generates predictions one by one. The decoder gets fed initial state vectors from the encoder, but the state vectors are then iteratively updated as the decoder generates a prediction for each time step.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from our previous model - mapping encoder sequence to state vectors\n",
    "encoder_model = Model(encoder_inputs, encoder_states)\n",
    "\n",
    "# A modified version of the decoding stage that takes in predicted target inputs\n",
    "# and encoded state vectors, returning predicted target outputs and decoder state vectors.\n",
    "# We need to hang onto these state vectors to run the next step of the inference loop.\n",
    "decoder_state_input_h = Input(shape=(latent_dim,))\n",
    "decoder_state_input_c = Input(shape=(latent_dim,))\n",
    "decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n",
    "\n",
    "decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_states_inputs)\n",
    "decoder_states = [state_h, state_c]\n",
    "\n",
    "decoder_outputs = decoder_dense(decoder_outputs)\n",
    "decoder_model = Model([decoder_inputs] + decoder_states_inputs,\n",
    "                      [decoder_outputs] + decoder_states)\n",
    "\n",
    "def decode_sequence(input_seq):\n",
    "    \n",
    "    # Encode the input as state vectors.\n",
    "    states_value = encoder_model.predict(input_seq)\n",
    "\n",
    "    # Generate empty target sequence of length 1.\n",
    "    target_seq = np.zeros((1, 1, 1))\n",
    "    \n",
    "    # Populate the first target sequence with end of encoding series pageviews\n",
    "    target_seq[0, 0, 0] = input_seq[0, -1, 0]\n",
    "\n",
    "    # Sampling loop for a batch of sequences - we will fill decoded_seq with predictions\n",
    "    # (to simplify, here we assume a batch of size 1).\n",
    "\n",
    "    decoded_seq = np.zeros((1,pred_steps,1))\n",
    "    \n",
    "    for i in range(pred_steps):\n",
    "        \n",
    "        output, h, c = decoder_model.predict([target_seq] + states_value)\n",
    "        \n",
    "        decoded_seq[0,i,0] = output[0,0,0]\n",
    "\n",
    "        # Update the target sequence (of length 1).\n",
    "        target_seq = np.zeros((1, 1, 1))\n",
    "        target_seq[0, 0, 0] = output[0,0,0]\n",
    "\n",
    "        # Update states\n",
    "        states_value = [h, c]\n",
    "\n",
    "    return decoded_seq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Generating and Plotting Predictions \n",
    "\n",
    "Now we have everything we need to generate predictions for encoder/target series pairs that we didn't train on. We'll pull out our set of validation encoder/target series (recall that these are shifted forward in time). Then using a plotting utility function, we can look at the tail end of the encoder series, the true target series, and the predicted target series. This gives us a feel for how our predictions are doing.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder_input_data = get_time_block_series(series_array, date_to_index, val_enc_start, val_enc_end)\n",
    "encoder_input_data, encode_series_mean = transform_series_encode(encoder_input_data)\n",
    "\n",
    "decoder_target_data = get_time_block_series(series_array, date_to_index, val_pred_start, val_pred_end)\n",
    "decoder_target_data = transform_series_decode(decoder_target_data, encode_series_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict_and_plot(encoder_input_data, decoder_target_data, sample_ind, enc_tail_len=50):\n",
    "\n",
    "    encode_series = encoder_input_data[sample_ind:sample_ind+1,:,:] \n",
    "    pred_series = decode_sequence(encode_series)\n",
    "    \n",
    "    encode_series = encode_series.reshape(-1,1)\n",
    "    pred_series = pred_series.reshape(-1,1)   \n",
    "    target_series = decoder_target_data[sample_ind,:,:1].reshape(-1,1) \n",
    "    \n",
    "    encode_series_tail = np.concatenate([encode_series[-enc_tail_len:],target_series[:1]])\n",
    "    x_encode = encode_series_tail.shape[0]\n",
    "    \n",
    "    plt.figure(figsize=(10,6))   \n",
    "    \n",
    "    plt.plot(range(1,x_encode+1),encode_series_tail)\n",
    "    plt.plot(range(x_encode,x_encode+pred_steps),target_series,color='orange')\n",
    "    plt.plot(range(x_encode,x_encode+pred_steps),pred_series,color='teal',linestyle='--')\n",
    "    \n",
    "    plt.title('Encoder Series Tail of Length %d, Target Series, and Predictions' % enc_tail_len)\n",
    "    plt.legend(['Encoding Series','Target Series','Predictions'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating some plots as below, we can see that our predictions manage to effectively anticipate some trends in the data and can understand when certain patterns should remain fairly steady. \n",
    "\n",
    "However, our predictions look overly conservative and clearly fail to capture a lot of the choppy variability in the data. We would likely stand to gain from increasing the sample size for training, tuning the network architecture/hyperparameters, and training for more epochs.  \n",
    "\n",
    "**Check out the next notebook in this series** for fancier architectures and more expressive predictions.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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W1qJnz54AALPZHPV4QkgHgQoAHMfFfQ9BEPR/Rx4/GpIk4YUXXsDcuXP1vwchBIIgoHfv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gAAAg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\nNvdpJM8oxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f523a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiSpLpPBlSQBGdnW4MpDY/2VuJKUU5OXI0mSJNVhMriSJIoyV22aBgOy7kqS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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f67b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 6007)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ArFnTkJFhQFpaKhYv/hAfffQBLlw4j4oVKyImJgZLly6HLMtYtGgeTCYjKlTw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f008da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_and_plot(encoder_input_data, decoder_target_data, 33000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
